Conference | Kami Vaniea

Symposium on Usable Security and Privacy - Day 3

Wed, 26 Aug 2026 03:01:43 -0400

Welcome to the happening in Hannover, Germany and being hosted by CISPA. The Symposium is a community that studies how people use security and privacy technologies.

Blog posts:

Session: Usable Security Mechanisms & Emerging Threats

Presenter: Yorick Last, Paderborn University

You can sign digital documents via electronic or digital signatures. Handwritten signatures work well but provide no guarantees of the authenticity or the integrity. Often because they are typed, or could be easily copied.

One way to make this easier and better for users is Digital Identity Wallets applications that collect various identity attributes, presented to the user in a card-based interface. These are decentralized user-centric means of identiy management. EU is rolling out a Digital Identity Wallet next year.

The project proposes such a prototype wallet: IdentitySign. Free and open-source web applicatoin, uses the Yivi identity wallet and lends cryptographic support from .

Had a user study including an unmoderated usability test with 99 participants recruited accross the EU.

IdentitySign was generally found to be useable. But some users did not understand why such a signature was needed over just signing their name with a (digital) pen. Only 20% could detect a valid signature.

So the researchers tried to address the issue of misplaced trust. Also added some friction to the UI for verification to slow the user down.

Ran the study again with 74 participants. Again from across the EU and an unmoderated remote user study. The scenarios included 1 signing and 2 verification tasks.

Usability was still considered to be good. 82% could now correctly find broken signatures. 95% correctly identified a signature as correct.

A key lesson was that the UI needs to balance being helpful without seeming to imply trustworthyness. It is important that users be able to judge if a signature is correct themselves.

Presenter: Xueyang Wang

The visual-physical discrimination vulnerability - the point at which humans cannot differentiate between real and faked. This is also known as the .

This research project looks at ways that an attacker can inject virtual objects onto the user’s vision.

Endogenous injection
- invisible duplicates of existing objects in the user vision
- users have trouble detecting these
Exogenous Injection
Type-deceptive overlay
- Modifying a part of the user vision. For example adding flowers to the top of a trash can so that it looks like a flower pot. The user can still see it but cannot figure out where the trash can is.
Attribute-overlay
- Adding elements like putting logos on a white t-shirt can impact user decisions. Even when a user knows that the image was fake and had been added. This is well in alignment with marketing research.

Attacks with the highest behavioral success rates received the highest plausibility, lowest workload, and highest usability ratings. In other words, if people thought the fake thing was real, they were happy to engage with it and were happy about it.

Research outcome: even if the user detects the fake, it still impacts their decision making and physical behavior.

Presenter: Katarina Galanska, Masaryk University

Interested in how recommendations were acted on. Security recommendations tend to be high-level and general so many possible actions could be taken based on them.

Suggestions for how to address a security vulnerability were written by humans and various of AI. Did a survey-based study asking users about the following three aspects of each suggestion.

Clarity
Actonability
Confidence

Lightning talks

Florin Martius, Registered Reports at SOUPS: Call for Discussion
- Registered reports: comprehensive research plan is submitted to the reviewers rather than the final research paper. The idea is to not waste research time, and create more high-quality research.
- Challenges exist though, it now shifts reviewing effort.
- Kami’s opinion: Contextualization of results is important in a final publication. Because it is not just about the methods. It is also about the interpretation of the results which cannot be done up-front.
Simon Althaus, PIONEER - A PrIvacy companion for mOtivatioN and knowlEdge transfER
- Rough motivation is to make it easier for users to handle privacy
- App involves privacy and lessons and motivations
- Can compare apps to each other to make decisions
Christina Detsika and Julia Justen, Age Groups for Security and Privacy Studies with Children
- Deciding what age ranges of children to do research with is a bit complex because developmental stages can drastically impact cognitive understanding. The work aims to determine suitable age ranges for research in the USEC space as well as how to best talk about the population choices in research papers.
Camille Cobb, How do we encourage students (and ourselves) to take meaningful action against dystopian technologies?
- Advocating action-oriented tech ethics education
- Current ethics materials focus on how to make judgments. But not much about how to take action after the judgment is made. Especially when the action is professionally risky or it is unclear how to do.
Nicole Dircksen - What should security security researchers do in an ethical situation.
- Research project is aimed at understanding where researchers agree and disagree on what is ethical in the field. Aim is to better understand our current understanding to advise new rules.
- For example: researchers find a vulnerability in a medical device where the company is now out of business. Should they disclose so that hospitals know and can remove or better protect such machines BUT also inadvertently tell attackers about the issue. Or should they stay silent and hope that attackers do not also find the vulnerability.

Session: Scams, Fraud & Misinformation

Presenter: Smirity Kaushik, The George Washington University

$3 billion is lost to social media fraud in 2024 ( ) and short form videos are popular with the general population. This also leads to a whole ecosystem of influencers.

RQ’s

What types of scams?
How do users report scams?
How do TikTok users identify these steps?

The researchers manually searched user-reported videos for scams. Found 150 videos (after reviewing them).

Types of scams:

Financial
Influencer targeting
- asking influencers to do things like buy a product and review it, but the influencer has to buy first before getting compensated.
- cloneing an influencer and then asking for things. Causes the influencers to have to post about such scams
- Deepfake of an influencer to promote a different product than the influencer initially promoted.
Shopping
- Fake or poor-quality products
- Goods never delivered
Account
Romance
Miscellaneous

How do scammers entice people?

Lots of use of direct message and “for your page”
Scammers used “need and greed” or the “distraction” principles to lure victims

How were scams identified?

Found the same as the FTC common approaches like request to pay right away
Also found that scammer communication patterns, account discrepancy, unauthorized transactions, product and price mismatch

Kami’s view: this is reminding me of our paper on , I think that we need more research that looks at what people say after falling for a scam organically, that is what they write immediately after they fall for the scam, as opposed to what they say to a researcher which may be biased by the presence of the researcher.

[Source-Level Disengagement: A Usable Security Defense Against Misinformation](Source-Level Disengagement: A Usable Security Defense Against Misinformation)

Presenter: Bogdan Carbunar, Florida International University

Misinformation is a serious issue and even if users are aware it can still influence thinking as shown by the earlier paper today on MR and changing what users see.

This research targets the issue that the user is seeing repeat misinformation from a person they are following and are not unfollowing.

Three interventions

Inline Warning
- Warning below main post
Action Controls
- Warning followed by buttons like block and filter
Blur Overlay
- post cannot be seen without clicking
Control
- no intervention, just the post content

29 participants in a within-subjects study. Processed 4 timelines each of which had 12 posts (3 with misinformation).

The control condition had only one user do something about the misinformation. Bu tfar more with the other options, particularly the action controls option where controls were made readily available.

Users sometimes chose the blur or filter options over other actions like unfollowing because unfollowing leads to sending social signals and breaks a relationship.

B luring still caused participants to view the content anyway, and then took an action.

Kami question: I wonder if this finding would persist in the real world. Earlier comment is about filtering and blurring to avoid content without breaking a relationship. It may be that in the real world users get to know sources and therefore do not open the content because they know its likely shape already.

Design Implications:

It should be easy to act when misinformation is encountered, not just warned, users should have a clear path to act
Preserve relationships should be possible. Users should have silent blocking ability where the followed person does not know their content has been hidden. Users are more likely to take this option
Design for limits - platforms also need to take actions themselves. Users get overwhelmed, so the platform itself needs to reduce user burden.

Presenter: Nathan Malkin, New Jersey Institute of Technology on behalf of Zane Xu

Survey research is starting to have problems with LLMs giving answers instead of real users. This is super bad for science. Attention check questions like: *“please answer `strongly agree’ to this question” are irronically easier for LLM than humans are are more likely to be correctly answered. One option is questions that LLMs answer poorly, but well those are vanishing. We could also just ask participants “please do not use AI”, but well that doesn’t really work.

This research

Can we detect LLM
How often
What can we do

So a survey was designed…

\[scarcasum\]

Collected metadata like keyboard speeds, page timing, lots of things
Collected 250 responses across both Prolific and MTurk, including a “Please don’t use AI” situation.
Asked an AI agent to also fill out the survey to provide a baseline.

Recommendations:

Do not use MTurk.
- 84% of MTurk responses were LLM
- vs 8% on Prolific
They tracked what participant copied and pasted
- If they copied text from the page, and if they pasted text that was not already present. Also if they pasted text that they did not type.
- This is the main way to differentiate ….
Could we we disabled copy/paste?
- MTurk users re-enabled it or typed in gibberish
Asking users on Prolific to not use AI actually worked
- 0% used AI
- Agents were also less willing to fill out the survey if it said not to use AI on the survey.
Asking for self-report AI usage
- Agent is happy to state that it took the survey
Keep reading the answers you get
- Detecting AI by hand is possible. It is not the content, it is the style
Consider using open-ended attention checks
- Distinctive answers by AI. For example “what is the shape of a ball?” Humans say “round”, AI says “spherical”

These methods work now, but are unlikely to last. AI is changing quickly.

It is important that as a field we work with other researchers to solve these problems. Individuals cannot solve this alone. Data quality checks need to be discussed in paper, because we cannot assume good data for online surveys any longer.

Presenter: Wellington Esposito Barbosa, The George Washington University

Online surveys are prevalent in data collection. But detecting fraud in such studies is very hard. Researchers have to know what real data looks like and what fraud looks like.

Research papers surveyed are coming from non-security communities and is not necessarily being framed as “fraud” the way security would frame it. Researchers often discover fraud while doing other data processing or review. This can be very expensive if the study must be terminated or abandoned.

RQs

What is research fraud?
- No consistent definition among papers.
- Some definitions:
  - Intentional data fabrication
  - Careless and Insufficient-effort
  - Automated, AI-mediated
- Fraud behaviors found
  - Multiple accounts, duplicate responses,
At what stage in research are researchers trying to stop fraud?
- Mitigation strategies are being implemented late stage in cleaning data or were very fragile like asking attention checks.
- For example if a participant fills it out twice: could be blocked based on IP address.

High level point: similar to other aspects of security, we see researchers selecting protections that may not align well with the threats.

Q&A

How can we setup an early warning system for when survey detection methods for bots start being less effective. The publication system is far too slow, and many people need to know these.
- It is a concern that if a “how to avoid survey fraud” was put together, it would likely only be good for a few months.
- We are trying to build a survey system that has security features built into it. And then make it easier to put things that work into it directly.
Double problem: participants are using AI to fill out surveys AND researchers are using LLMs to simulate users in some research. How have we already reflected on the ethics of the means of identification of fraud?
- There were no real discussions of ethics. There were some concerns about how methods were detrimental to reaching specific communities, but it sounds like this was discussed more in terms of research integrity and getting data from a range of users being important.
Should we all go back to in-person surveys? This will slow research. But given the speed of paper submissions, maybe slowing down is a good thing.
- There are pros and cons to that. But we were surprisingly pleased by how effective current mitigation are.

Session: GenAI Privacy & Risks

Presenter: Jonah Bellemans, DistriNet, KU Leuven

Kami was busy and missed most of the talk.

Understanding U.S. Users’ Security and Privacy Transparency Needs for Consumer-Facing Generative AI

Presenter: Jiaxun (Messie) Cao, Duke University

Users have poorly informed mental models about how GenAI is using their data. This includes things like company chat bots.

Most transparency information is expert-facing and aimed at explaining model capabilities and how it uses data.

RQ’s

Do users consider S&P concerns when picking a model?
Current practices and challenges
User preferences

21 interviews with users and 20 designs.

Initial adoption was initially driven by non-S&P factors. Instead popularity, utility, and price were most considered. S&P was a desired adoption feature, but hard to get good data about so not considered.

S&P uncertainty became a larger concern based on the content of the planned discussions with the AI.

When people did read privacy statements, they see it as lacking credibility (8/21) which means they did not trust it. This is a very similar result to what general privacy policy research on how people read them. From my memory: “privacy policies are designed by lawyers to make it hard for me to understand and beneficial to the company.”

User wanted independent evaluations of the privacy policies. Somewhere they could go to compare privacy practices and that had the skills to properly read the practices.

Kami’s thoughts: Consumer Reports in the USA does now include commentary on the privacy practices of some products. Not all, sadly, but their comments on baby monitors regarding privacy are rather amazing.

Presenter: Jennifer Vander Loop

When a loved one dies, those left behind may want to be able to talk to the deceased as part of their digital legacy. Some older people are starting to consider their wills and considering how they want their data used after death, including how they do or do not want to be emulated by a Generative AI.

People were interested in digital resurrection, but less ok with the idea of immortality where such things were used indefinitely.

Ran a survey on 900ish people on Prolific. Looked at what people wanted for themselves and what they wanted for others that had passed. Many people also thought about how creepy this is. Disconnect between teh voice, person, and after-death content. It may sound like them but the content might not match because it did not think like them.

Participants came up with many possible harms. Scams were a large one, they did not want a loved ones voice used to scam others. Also harms to the person’s reputation, and social standing. There is a risk the company might use this for their own befit, such as suddenly getting a loved one to market to you. There is also the risk of non-aligned models where there are two versions that were made with different models. The model itself could be held ransom from the family, especially if an emotional attachment had been made.

Session: Security Advice & Education

Advice given to users can be straight forward, but lead to lots of extra questions around how to opperationalize and understand the advice. There is a difference between being able to find the advice, and the ability to follow it because it can be too complex and make them overwhelmed.

Experts view themselves as a good source of advice, which is partially true, but they are not necessarily good at providing advice for users that is easy to use.

Usability survey used with about 350 participants.

Designing an IT security advice usabilty scale. Started with the , mildy modified to Information Security Policy Usability Scale then resulted in a Adaptation for consumer Advice. The final scale is similar to the SUS in that it is a short 10-question scale.

Older people perceived the advice as more usable than younger people did.

Normal advice like sentence length had no positive advice on usability. But there was some improvement from having clear steps, low tech words, and context alignment.

Consider adjusting advice based on the audience.

Presenter: Farida Chowdhury, BRAC University on behalf of Utsho Das and Argha Pratim Sha

CyQured board game was developed for cybersecurity education.

Security at home is a human decision problem. Users have many devices, no security team at home, and traditional training does not provide practice. So how are home users going about learning about security.

Related work

Many others already on

The game was modeled off of Monopoly. Used STRIDE in a simplified format. There are 16 device cells such as laptops, router, NAS. Placement is also not guesswork.

Conducted a pre-post evaluation with a security knowledge test before and after an hour of group game play. Some participants were also interviewed.

1 hour of play did produce substantial knowledge gains. People with pror security experience did gain more, but everyone learned.

Nice to Know You’re Not Alone: Co-designing Community-centered Online Safety and Privacy Education with Librarians

Presenter: Florian Schaub, University of Michigan on behalf of Tanisha Afnan

People struggle to stay safe online:

Lacking privacy and security literacy
Do not seek help of security experts

Pror work at PETS:

Found that people who are trying to help others, such as librarians, have challenges moving past intro sessions to get people to implement help

In this work, focus was on helping librarians in doing community engagement.

Conducted 4 participatory design workshops with 14 librarians. Participants had 5-20+ years of experience teaching a range of workshops.

Privacy and safety are sometimes taught specifically, but they are often part of a broader digital literacy. They struggled a bit with how to balance conceptual issues with practical skills. This group also has limited resources, such trainings are only one part of their job.

There are many existing educational resources out there that are often diffused and hard to find. Paper has long list of existing educational materials. Existing resources were not easy to just adopt. Many were aimed at experts. Many had loads of jargon. There are also limited resources in non-English. They also wanted to print resources and many are not print-friendly. So most of the time these materials were used to help inform the teacher rather than the learner.

Co-designed solutions for learners:

Interactive games came up
Take-home worksheets and practice exercises
Printed handouts and low-tech materials
Non-class based educational offerings
- Like a monthly community challenge
More need for professional development
- Kami: reminds me a bit about how tackles the issue of teaching Computer Science in schools and taking the view that educators are good at their job, what they need is assistance in making a plan and organizing resources.

Kami note: reminds me a bit of the project that supports older adults in Canada.

Security versus Productivity: A Case Study of Email Management Practices with Job Task Analysis

Presenter: Philip Shumway, University of Tulsa

Cybersecurity policy is written at the higher levels of an organization. That policy then becomes tasks for lower level employees.

Goals:

Re conceptualize security as embedded work
Identify and map task-based tension
Understand how work done

Task Analysis is derived from Job Analysis. Jobs are broken into tasks, and tasks are broken into attributes.

Looked into a case study of a medium sized publisher that had fallen for a phishing attack. So they hired a full time security person to help them be more secure and do security training. Researchers identified a large number of tasks that employees do, broke them down to a smaller set, and then had the employees rank the tasks.

Employees do not see security and productivity as the same. Employees saw getting it wrong more serious for security.

People who self-report having more external emails also spend more time on security.

One interesting thing about this work is the division of employees based on their security context. People have different security needs if they get lots of external emails, than if they did not.

Kami note: Reminds me a bit of the paper which also asked employees to rank security tasks as a way of communicating culture with others.

Security and Human Behavior (SHB) 2024 - Day 2

Wed, 05 Jun 2024 00:00:00 +0000

The lists all the speakers and the sessions they are in. There is also an that lists some recent works from each person.

I am live blogging the event and then doing some clean-up afterwards. So some entries may have poor spelling or not flow as nicely as I might like. I plan on doing a post-event pass to do some cleanup and add links.

Contents:

Session 5: How People Think

Presenters: Yi Ting Chua, Manila Devaraja, Christian Eichenmüller, Richard John, Allison McDonald, Sergio Pastrana, Ryan Shandler

Yi Ting Chua

Title: Security and behavioral Evidence

Evidence is no longer just physical, now it is also digital since technology exists throughout our environments. So if a crime happens, not only does physical evidence need to be collected so does digial devices. These devices need to be identified as part of evidence collection. Digital forensics is really just a branch of forensic science.

Evidence and traces collected via digital devices are now also used beyond crimes. LIve streaming events, for example. Such as broadcasting or recording a protest.

What does all this mean for the court system? Such a system has many stakeholders including: judges, prosecutors, defense attorneys, jurors, and witnesses. Several of these groups need to analyse and sort through the data.

Manila Devaraja

Smartphone permision settings

Permission settings are the primary way to manage privacy on smartphones. But there are allot of permission settings. And those settings and meanings do change over time. Thankfully devices, including iOS, have various features to help users understand them such as privacy report. Also the automatic removal of permissions.

Research challenges: What do users think a setting does vs what it actually does? How do user preference vary? Could users be clustered for personalization? How do socioeconomic factors influence privacy preferences and understanding?

As a side note, users are starting to use LLMs when filling in open text boxes.

Christian Eichenmüller

“My whereabouts, my location, it’s directly linked to my physical security” The spatial digital-safety strategies of at-risk users

When Snowden met with Glen Greenwald they first took the batteries out of their cell phones and put them in the fridge. They did so because a fridge is a ferdey cage that blocks signals. Christian is very interested in changing spaces. By blocking phone signals the space they are standing in changes.

Conducting a study using interviews to understand the spaces people are trusting or considering to need protection. The types of trust needs and assumptions differ a good bit by person and that person’s threat model. Their trust of providers can be quite different depending on their threats.

There are “unknown unknowns” and “known unknowns” and “unknown unknowns” are more challenging for users because they do not have many options. Managing location privacy is also tricky. Some people leave their phones at home when they go certain places. People think allot about about where devices are physically located. The control what devices are allowed in some spaces, they also choose not to enter spaces that have devices they do not like or where there is a risk of such devices.

Data-rich and data-poor environments. These can be intentional to get a bit of peace. Or it can be caused by governments say shutting off the internet.

Richard John

Title: Moral Foundations of Armed Conflict

Consideration of the armed conflict in the Ukraine. They looked at how people world wide consider the conflict.

(MFT; Grahm et al.2013; Haidt & Joseph, 2004) was designed to explain both the variety and universality of moral judgments (Graham et al., 2017)".

Looked at English-language tweets oer first 36 weeks in the US, NatO countries, Ukraine, and Russia. Used moral foundations discretionary ( ) to analyse.

The conflict was conceptualized around loyalty and care. Some about fairness.

Allison McDonald

Title: “Delete it and Move On”: Digital Management of Shared Sexual Content after a Breakup

Generally engaging in sexting is a good thing for inter-personal relationships. But not every relationship lasts and there are lots of risks that can happen with type of content if the other person who holds it is no longer trusted.

Interesting question of how people use these tools who don’t think they need protection at the time they share, but then later do need them. Conducted research about how people thought they or their partner might handle such media after a breakup. Also how do they think about co-created content.

We know from prior work that people are sexting where they already engaging in chatting like SMS, Messenger, and WhatsApp. 75% of people saved sexts. 64% were proactively saving such as downloading to camera roll, dedicated folders or locked or hidden folders. Surprisingly after a breakup only about 55% of people would want the other person to delete the images. 11% hoped the other person would keep the content: “save it and remember what he misses”. Most people had not had a conversation with their partner about managing such images, and most that did have that conversation had it after/during the breakup.

People viewed the content as meaningful (~33%) and therefor wanted to keep it. While 2/3 would prefer it was deleted.

Challenging question of how to technically operationalize shared ownersship of media. Especially in cases where that media may have been stored elsewhere. People also keep this content over time including when they change devices. How do we as designers manage the storage of sensitive content over time.

Sergio Pastrana

Title: How people think… or how they share their thoughts in times of war

In February 2022 Russia decided to invade the Ukraine. Russian government declared this as a “special operation” and evne banned the word “war” on social media.

So how do people react to the bans? They start looking for external channels. Guidance on how to do things like give reviews on Google Maps of Russian resturants which are actually comments about the war.

We sudied the unconventional use of online services to bypass censorship. Such attempts were common at the start of the war. Topics being discussed more after the war started included: fascism, information, slurs, violence, and warlike.

When western people tried to bypass to talk to people in Russia they tended toward two topics: (dis)information and censorship bypass. There were also discussions around humanitarian help, hate speech, and travel advice (how to get in/out).

Platforms didn’t really like having this war-related content being posted on them because it was not their primary purpose. TripAdvisor monitored and removed content about every 2 days. Google Maps actually disabled reviews for all sites in Russia. They also did a mass removal of many posts, not just war-related.

Ryan Shandler (Georgia Tech)

Title The Insidious Consequences of Cyberattacks: An experimental approach

George runs experiments that randomly assigns people to experience cyberattacks in controlled conditions to see how these impact people’s views.

The impacts of cyberattacks are not just the close-in ones. It also includes the long-term imapcts. These long term consequences can have severe psychological reactions that lead to issues like having views about how the world will end because of a cyberattack.

Used actors to create fake media broadcasts to present information about how. Lots of interesting methodologies to test how people react to attacks in real-world situations. Lots of ethical review done around all these studies. We have a duty of care to people to understand what they experience so we can help them.

Even a simple cyberattack can cause severe stress.
Exposure to cyberattacks reduces trust in government and trust in cyberspace. Distrust in protections of societies.
People are willing to sacrifice civil liberties for security as a function of their exposure to cyber threats.

Ryan believes that the accumulation of cyber attacks over time is having some serious impact on political alignment and views.

Session Discussion (Q&A)

Q: What is the moral foundation we are forming in countries around war.

A: Its surprising how similar the moral foundations are across regions. Ukraine for example was using more care words.

Q: To Ryan. I wonder if these concerns are linked to prior experiences. There are large known impacts on public hospitals or other events.

Ryan: We have looked. There is a spike in stress after large events. We do find that people closer to the event experience that stress more. But to properly study we would need before/after data which is harder to do with real-life events.

Q: To Ryan: The first-order impact on people may not be the goal. Instead long-term stress (third-order) impact may be the real goal for the government behind the attacks. We know that the Russian ransomwar instead of ransomware because the goal is not to lock down data, it is long-term psychological attack.

Q: To Alison. How does what people want to happen with sensitive images change before and after a breakup.

A: I was expecting those that had a breakup to be more restrictive, but that wasn’t the case.

Q: Was there any mention of the partner/ex-partner’s security practices. Images are often lost due to attack.

A: (Allison) No one mentioned it.

Q: Sources of distress. Shock? Loss of device? Are these cognitive effects vs physical effects.

A: Usually its a “creeping dread” that something bad is coming and I don’t know how to protect myself. Beyond that, it depends on the type of attacks.

Q: To Ryan: What are your views on mitigation? When we talk about disinformation (which is different) and how talking about disinformation cause less problems then talking about the amount of disinformation. Is it similar in cyber attacks: 1) are people distressed in proportion to the attack type, 2) do you look at how governments discuss the issue such as discussing with public before hand vs after the attack.

A: Cyber attacks are different from disinformation. To deal with cyber attacks we need to be talking more about them. People closer to the attack were less worried because they saw the saw hospital and saw that everything was fine. Vs people far away were imagining all sorts of things out of movies and were therefore more worried.

Q: For Christian. Do people understand that governments get data from companies?

A: Trust in entities is tied to the threat model. People do ask themselves how a state might interact with companies. For example, participant from Iran said that Google was ok to use because Iran government do not get along.

Q: To Alison. People share sexual content partially because it is risky and that helps with trust. Anything you do to mitigate how risky an experience is, that won’t help if the purpose is partially to be risky. Important to consider because the user may be seeking risk. Also consider pre-nuptial agreement research that study un-romantic up-front discussion.

A: It is very hard to minimize the risk where the sharing does not feel risky. So the risk would still be there.

Q: To Alison. Demonstrating capability for safety is something that is part of relationship building. Because being safe is a way to signal trustworthiness.

A: We did measure security posture. It did not correlate with their sexting protection behavior.

Q: To Yi Ting. Have you talked to people who are involved such as expert witnesses. Several people in the room have been expert witnesses. There has been work on how to best educate people like judges and lawyers. There is quite a mix of knowledge levels.

A: There is not allot of research on the percentages of say lawyers who are or are not knowledgeable. They also learn on case, where if they are on a case they do a good bit of self-education.

Q: To Richard. Do you differentiate between cyber exploit and a cyber attack?

A: The public does not differentiate. They just see it as an attack as it is described by the media.

Q: What contextual information about prior attacks did you collect.

A: As much as we can get, close to all of it. Extensive surveys with people.

Session 6: Privacy

Presenters: Alessandro Acquisti, Laura Brandimarte, Serge Egelman, Susan Landau, Alena Naiakshina, Sameer Patil

Alessandro Acquisti

An infrastructure for a study <- took nearly 5 years to design

Industry says that behavioral targeting is good for consumers and sellers because it makes it easier for consumers to find products. So better for economy.

Question: Is behavioral advertising economically beneficial (privacy asside).

Study Design: Strict privacy controls:

Informed consent
De-indentified
Encrypted

Starting with Facebook ads. Participants who click on the ads get screened. Then sent to survey if the screening passes. Then we get them to install a browser extension. Complex technical backend to process and store all the data coming in. When the study starts in the fall we expect to get about a Petabyte of data.

Data is very sensitive, so lots of ethics discussions. Some protections put in. We blacklist some sites like GMail, Google Drive, so data is not collected. System also tries to strip PII. Communication channel that hides participant identity.

System collects: all the URLs visited, all the ads they see, the HTML for some websites. From email we get shopping and promotional emails. We don’t collect information like credit card. But do collect that they bought something and from where.

Original question was ad-blocking, ad-tracking, and similar. But we now realize how much usage is possible with this data.

Laura Brandimarte (University of Arizona)

Title: LLMs and Creativity

Motivation: Can AI-Generated Text be Reliably Detected?

Currently we (researchers) are terrible at detecting AI-generated text. People are also bad at it. Its even worse because GPT detectors are biased against non-native speakers.

Idea: can we find features of human creativity that would help distinguish between humans and LLMs. Because LLMs are just prediction models that create the most likely word or set of words that will come after the prompt.

Perplexity assesses each word prediction with equal importance. But in English we have bursty nature of language. Certain words or phrases are more prevalent in particular contexts, this is what current detectors look for. LLM output tends to have more repetition.

Some initial results are quite positive. Looking at unique ways humans write has good potential.

Serge Egelman

Title: The Medium is the Message: How secure messaging apps leak sensitive data to push notification services (to appear at PETS)

Misuse of push-notification APIs and how that is inadvertently sharing information with apps. For the purpose of batter saving, there are APIs that run in the background that will scan for new messages and “wake up” an app when there is a message for it. This means each app does not need to constantly pull for new messages.

Notifications can have payloads. You can put in the payload of the message any key/value pairs that you want.

How should this be done: (Signal does it this way)

Cloud notification message has empty payload
On receipt, OS wakes up target appear
Target app “phones home”, securely downloads message content.

In other words, ideally no sensitive data is in the payload. Android Developer blog recommends using the payload data to send things to make it more efficient. The problem is that if information is sent via payload then Google sees it and its open to say legal requests.

Serge wanted to know if payloads are being used in such a way that leaks data. The answer is yes. Looked at apps that advertise secure messaging and have at least a million installs. Not necessarily E2E. 20 apps looked at. Skype does not do E2E and even if the user asks metadata is still sent unenctrypted.

Ron Whden wrote a letter to General Garland about how the government is collecting data this way.

Much of Serge’s work is about developer misuse of security APIs. In his opinion this is an incentive mismatch issue.

Susan Landau

Title: How is the private sector using non-content transmissions of smartphones

After the Snowden disclosure of bulk call records. Then the president said not to worry because call content was not recorded, just the metadata. But metadata says allot of information, it can be used to predict many things about a person such as religion, all based on who they contact.

Smartphones are even more invasive because they also record location. Now that data can be very useful for things like urban planning, emergency planning.

But it is not just phone metadata… phones have GPS, accelerometer, proximity sensor, lots of sensors. The sensors are super useful to the user to make experience valuable. But all that data can be offloaded off the phone. Most software reports how it is doing back to the main server. That is needed for companies to detect problems so they can fix it. Data is collected almost without any controls. Notice and choice is also implausible, because you must be asked about each packet. Its hard for people to predict how such information can be used to predict other information.

Lots of existing research on metadata and telemetry collection. Everyone looks at collection, not much on use. Why? Its easier to see what is collected. If you want to know how it is used, you have to talk to a company, and they are disinclined.

To find out, we tried to look at patents. Though patents show interest, not intent. In other words, this is what companies can do but its unclear if that is what they are doing. 2500 patents studied (not all read). Found a stubhub patent measuring the leve. of a user’s enjoyment at the event to determine prices at a future one. Meta patent uses accelerometer data to determine two users had beeen repeatedly in close proximity on the same form of transport; when one user changes traveling modality and then proposes them as a potential contact.

Alena Naiakshina

Title: Those things are written by lawyers, and programmers.

Interested in the gap between regulators, privacy experts, and developers. GDPR (2018) for example can result in fines. So studied how privacy experts, developers, and team coordinators communicated.

Legal language is (unsurprisingly) complex for developers to understand causing them to have to reach out to team coordinators to have it explained. Developers struggle to differentiate between privacy and security (they are both cryptography right?). Privacy experts create documents and send to team coordinators, sometimes that communication is one-way, sometime two-way.

Privacy experts are not seen as technically knowledgeable. Which can make communications complex as much explanation is required on all sides.

Privacy requirement verification is also an issue because its unclear how to do the verification. Developers can’t implement because they don’t understand the goal well enough to right a test to see if it was reached the way the privacy expert wanted it to be.

Also looked at how developers implemented privacy requirement. They did this as a lab study where in some conditions they had access to a privacy expert. The researchers found that developers rarely reached out to privacy experts. Though they also commented that the privacy requirements were hard to understand. Experts were usually contacted at the end once the solution was built to see if it was done correctly.

Sameer Patil

The user-side of privacy decision making. If people find things/apps/devices “creepy” why are they still using them? If there is regulations coming in, then why do apps continue to be creepy.

Users have more or less resigned to having their privacy invaded. They say things like “that is the price to pay to use the app” or “yes, but what can I do”. They feel like there is no other option. Allot of research in the area are about empowering users. WE find that that empowerment is embedded in a larger power over the users. For example: Facebook gives you control over who can see posts, but that exists within a larger Facebook ecosystem where users have few choices.

Decision making is also being driven by the larger expectations about app or other technology behavior. Users learn that apps take data and are “creepy” so they just assume this is true for all apps and that view is hard to shift. So if all apps are creepy, then what incentive do app developers have to be not-creepy if the user expects the creepiness.

Ongoing projects:

Started collecting emails that are meant for some other “Sameer Patil”. Interesting content in the emails. All sorts of things in the emails like banking transactions, bill payments, two factor authentications, and other things. Many attempts to stop the emails such as “unsubscribe” or emailing the sender. Sometimes take over the account and try and delete it if causes enough problem. Lots of experiences that are being written up in a paper.

Session Discussion (Q&A)

Q/A: Onging tensions between developers and legal teams in regards to privacy requirements. Several people have tried building tools to help the developers better understand laws. Though its tricky to convert “its complicated” in law to clear checklists for developers.

Q: For Susan. Did you see on the date of the patents any indication that its information being handed off from the NSA

A: Not really. Most patents fit in the types of analysis the companies were already doing.

Q/A: Putting research into Lawfair is a good choice. Its a good way to get information in front of policy makers, lawyers, and journalists.

Q: For Serge. Can you elaborate on what “third parties” mean. For example, I got a new car and now Signal messages are showing up on the dashboard.

A: That data is being processed on your phone. It is possible the car is scraping what is presented.

A: (Susan) Organization called “ ” that take academic work and edit it and put it up on their website. They put it on their website and then it is free for any journalists to use world-wide for free.

Q: To Alessandro. Your work is awesome. I’m curious about some of your questions. Also how are you going to select the participants in terms of representation and how managed over time.

A (Alessandro): It is allot of work and we do want to make it available to others. The infrastructure can also be made available, though it would take lots of maintenance to run. The research hypothesis, it started about ads, but then we realized we needed a platform that might be more useful for research.

The initial research looked at advertising. We believe that existing research is producing some research that is not taking into account key points. If you study one ad campaign they you may not see situations where budget is being relocated elsewhere. Behavioral targeted ads have been shown to include lower-quality vendors that cost more than can be found in search. Which would not be good for consumers.

Q: For Alessandro. What evidence would be needed to convince advertisers that behavioral advertising does not work. What kind of evidence is needed to convince them to use a less privacy-invasive approach.

A: (Alessandro) Assuming that our assumption is true (study not yet run). Then it would take several studies from different groups. Just this one probably won’t do it. It could well be that if all marketers move over to behavioral advertising then it is possible that they see the same percentage of engagement as before once everyone is doing it. For research, we need to consider more wholistic view, not just local results.

A (Serge): You will never get someone to understand something where their salary depends on them not understanding.

A (Adam): The other problem is that Facebook makes their money off of this. They are disinclined to accept such results.

Session 7: Wickedness

Presenters: Max Abrahms, Luca Allodi, Miranda Bruce, Ben Collier, Sunny Consolvo, Diana Freed, Anh V. Vu

Diana Freed

Research on youth and their concerns and threats.

(Google)

Long term interest in advice we give to end users and how experts assess that advice.

Luca Allodi

Cybercriminal enterprises as tech startups. How do criminals sort out all the technical aspects.

Looked at Genesis market. Different online markets have different features such as how involved moderators are, and how vetted the participants are.

These online ecosystems are evolving. Its moving away from following communities. More focus to services. More transaction elements move off of the forum such as onto Telegram.

Eindhoven Security Hub - infrastructure to analyze lots of data

Max Abrahms

Title: Operation AI-Aqsa Flood: Resolving the Puzzle

Interest in how governments are sometimes forced to perform actions. So things like does terrorism work. Had some good models…then the Al-Aqsa Flood happened and it upended the models.

It used to be assumed that people turned to political violence because it is effective. But that doesn’t necessarily match observational reality. Earlier research showed that violence against government was more likely to be effective than when targets were indiscriminate.

The latest situation in Israel and Palestine is leading to some higher level victories for Palestine in regards to external sympathy and recognition of their state. There is an interesting dynamic about claiming violence against military vs civilian targets.

Miranda Bruce

Title: Mapping the Geography of the Profit-driven cybercrime

Interested in the “local dimensions” or the “human element” in cybercrime. Focus is on profit-driven cybercrime. Studying the area allows us to develop global metrics, and better understanding the drivers of cybercrime. Understanding the conditions to create a cyber crime hub is helpful long term.

Locating cybercriminals is hard. To get around the problem they surveyed the experts such as active cybercrime intelligence officers. Started with a core group of known experts and used them to get references for other top experts. Also lots of cold calls to ensure experts were represented worldwide.

Survey looked at the: level of activity, impact, technical skill.

Found that cybercrime originates in a wide number of countries 90+. But only 6 appears on all indexes.

Ben Collier

Title: Influence Policing

Ben has a new book: Tor: from the Dark Web to the Future of Privacy

Influence policing - social marketing including behavioral advertising. The government normally uses these to do things like get people to brush their teeth. The police also use this type of marketing.

Government does behavior change propaganda. Such as “Lock your doors and windows”. Then there is a move towards nudging. Particularly psychological nudges. Internet means that nudges can be targeted to specific sections of the population.

What do the campaigns look like? Campaigns look not only at how to target at-risk people with content but also their parents. Also targets potential friends of people who might engage in violence.

Anh V. Vu

Title: No Easy way out: the effectiveness of de platforming an extremist forum to suppress hate and harassment

Look at Kiwi Farms - one of the largest forums known for online harassment. Sadly tied to suicides and has been up for 10+ years. Then in September 2022 someone in London Ontario who was targeted by the forum for harassment. She started a campaign to get platforms to block the forums. This caused the forum to move to several platforms. But they were even blocked by T1 networks.

We wanted to know how effective these blocking efforts were. Unintended consequence of blocking happened: Streisand effect. Right after blocking attempts more people tried to visit the forum, likely to see what they were about. Looking at network traffic it is easy to see drops in traffic as each network dropped them. As of publication, the forum was alive and well having recovered.

When the forum was blocked, people moved to competitors and to messaging apps.

Taking down such a website is very hard. Especially when the owners are not arrested or otherwise prevented from bringing the website back up.

Session Discussion (Q&A)

Q: Is there some way to put measurements behind different security advice.

A: A part of the problem is deciding the criteria. Is it that the issue is common? Or how severe the problems are? So how do you rank advice across such diversity.

Q: For Miranda. Its notable that the global south isn’t represented. Also there are countries I would expect to see based on redacted data are not there.

A: We didn’t see an evidence of any biases in terms of ranking their own countries lower/higher and similar with countries that their country might have tensions with. Experts also opted out because they did not feel like enough of an expert.

Q: IN prior SHB we have seen presentations about organized crime. How much work have you done or planned around bringing in understanding organized crime.

A: Organized crime: organized, crime, and governance.

Q: For Ben. Thoughts on what is appropriate and not appropriate for governments to do? We are used to the surveillance. But this is behavioral targeted behavior change.

A: Well it is legal in the UK. (Discussion of legality in various countries.) Governments and law enforcement are a rare advertiser that can cause serious consequences like jail or even kill you.

Session 8: Past and Future

Presenters: Judith Donath, Simson Garfinkel, Matt Goerzen, Alexander Klimburg, Aileen Nielsen, Bruce Schneier

Judith Donath

Online, false news spreads faster and farther than true information. Why?

False stories are the high heels of the internet. Signaling theory is a model of how communication evolved around deception and honesty. It can be profitable to lie. But if all communication was false, then communication would be useless. So how does communication evolve to be reliable enough to function.

A reliable signal is affordable to give by those telling the truth and expensive for those that lie. Status, for example, an be signaled by buying an expensive car which is a reliable signal of having wealth.

Claim: Fashion is a signal of status in an information society. By “fashion” is anything you show to others. Fashion also needs to change to show ability to keep up. Fashions exist in all sorts of topics from clothing, pets, and art. To show status in fashion you need to have knowledge of what is coming next, and what will be the next thing.

Innovations are adopted because they have utility. Fashion is a status symbol. So it has more value when it has less utility. The less useful an object the more it distinguishes you. Fake news is similar. True news is posted by everyone, posting it does not help people stand out.

It is important to understand why fake information is spread. If people are spreading it for fashion-like status is the goal and the accuracy is not the point.

Simson Garfinkel

What is new with digitalcorpora
What is differential privacy
Agency

catalog can be used for research for free. Built to assist in digital forensics education. The point is to all pull from a single source and do comparable research. Also, there is no PII or illegal content. There are also solutions that can be used on request.

SAFEDOCS program was ended :( resulting in an 8 million PDF corpus.

Educational impact of Digital Corpora.

Differential Privacy: Persistence pays off. NIST SP 800-188: De-Identifying Government Datasets: Techniques and Governance. The guidance is a good citation on why de-identifying does not work.

NIST SP 800-226: nice graph for visualizing privacy vs accuracy for setting the privacy loss metric.

Matt Goerzen

Title: Some Pasts and Futures

Thought about how to create a framework around data manipulation. Things like baiting journalists, and targeted advertising.

The US census became concerned that there were risks to their primary purpose. Issues like questions about citizenship. So they needed to not treat themselves as a trusted infrastructure. So developed understanding of different stakeholders and what security in census meant for those stakeholders. Security for the census includes also the protection of the people taking the survey including their perception of security.

Became interested in trolls and white hats. Who is positioned to pen test social media?

Looked at the emergence of white hat hacking. Resulting in the publication: “ ”. Some concepts that came out included “security by spectacle” where experts worked with the media. Via the media they argued that the real enemy is negligent companies rather than attackers.

Exploits developed as a type of scientific proof of the existence of a vulnerability. It can be used for proof and also for attack.

Alexander Klimburg

Aileen Nielsen

Will privacy law change the privacy environment? (And can it?)

Current US privacy law is trying to protect against:

Lack of transparency
Lack of control
Surfeit of Friction

General approach is to create more user rights, especially in regards to technical affordances. Such as ability to download in a common format. Now we will be getting Global Privacy Control (GPC) and data access/portability rights.

Do these affordances impact the privacy of users?

Global privacy controls: have achieved some legal recognition. The Global Privacy Control is also growing.

Questions: now that there are legal teeth, does that change adoption of the affordances? If we had to pay people, how much would we need to pay?

Also what is the success of the entire statutory scheme. This is where we get to data access portability rights. Does different legal status impact the data that is being collected and stored.

Bruce Schneier

Captchas: I collect caption jokes, please send.

Trust and AI. Trust is essential to society. The fact that we don’t notice the trust shows how much it is working.

Humans will make errors and assumes AI are friends rather than services. And companies will take advantage of this incorrect view.

Trust is an overloaded word. Interpersonal trust is like the trust of a friend. There is also social trust, which is trust in predictability. For example, Uber made taxi driving safer via stars.

We regularly make category errors around trust. Governments, corporations, are services not friends. We are about to make this same category error with AI. We may trust them, but will they be trustworthy. Did your chatbot recommend a vacation because it is best for you? Or because the company wants to sell those vacation packages.

AI personal assistant will work best if it knows everything about you which will make it effective but also quite scary. And we will start thinking of it as a friend that can be trusted. It will act trustworthy but it will not be trustworthy.

Bruce has been pushing by public AI models. Something that will require public accountability not just company business models. We can have AI that are agents, not just double agents. It is essential that governments help with this since governments have the potential to create social trust.

Session Discussion (Q&A)

Q: I have an Amazon Alexa that recommends things and I say “Don’t tell me about X again” and it says “I am sorry, I don’t understand”. And I realize that I am in an abusive relationship :P There is some help in thinking about AI as people, but people!=friend. There is that annoying person. Thinking of Alexa that way may help.

A: Because the devices are so relational, you can feel pressure from machines. We are susceptible to trying to please machines.

Any public system can be co-opted. For example, an Alexa can turn on and off lights. There is also an alternative called Home Assistant which is supposed to be free of surveillance. But there is now a company behind it because governments don’t have money for maintenance.

Q: To Aileen. Laws are also dependent on enforcement. If you look at the number of VVPA cases filed. Leading to tracking being removed because there are lots of litigation being filed.

A: (Aileen) I wonder synically if this is a moment in time and it will decay. There is not always impact from enforcement. If you have to enforce against each firm, that is too expensive.

Q: Ownership. So who owns my Amazon search history. Is it me, is it them?

A: Long term question, particularly in regards to LLMs. Lots of companies have been re-writing their contracts because of things like LLMs. There are some moves to start treating data as property. But this type of information is really co-creation. I write a post on Reddit, but Reddit itself created the platform, the moderator put in work, and people reacted. It isn’t just the author.

Q: For Judith. I love your framing. How does fast fashion fits into that framework.

A: Fast fashion is very interesting. Fashion started in roughly 14 century. Fashion moved in about a year because that is how long it took to travel. The rate of information movement impacts the rate of fashion change.

Q: Data liberation front from Google where you could download your data and then they deleted it. The willingness to accept model: you can come to us and we can do business but then you also have the right to leave.

Q: Fashion trends. There tends to be shame if you are doing things that are out of fashion. Is there some way to make misinformation unfashionable.

A: There are two risks with fashion: 1) the cost of being at the forefront is riskier, people might not like your fashion, 2) but at the tail end it is less risky but no one is following you.

Q: Going low is a way of counter-signaling.

A: Its another fashion-related piece. Imagine a world where you can signal high/medium/low. You don’t want to be copied by someone below you. So one strategy is to signal low because they can get away with it.

Q: Hypothetical. Imagine that in the future AI gets so good that bad actors start using it to cause harms at scale. Such as fake news read out by a real news anchor (deepfake). Should we reach such a future, and only solution is to use piles of biometrics, but GDPR stops us. Then how do we save the world?

A: AI is already used at scale in cyber crime. Its an arms race. I doubt that GDPR will block.

Fake news and fake photos have been around for forever. This has been around for a long time. Better phishing emails are not always the answer, sometimes bad phishing is intentional to capture the types of people most likely to fall for the phish.

Weirdly the demand for reliable information is actually rather low. How do you raise the demand for reliable information? For example, building up local news. Current global news that isn’t about them causes them to treat it as entertainment.

Security and Human Behavior (SHB) 2024 - Day 1

Tue, 04 Jun 2024 00:00:00 +0000

The lists all the speakers and the sessions they are in. There is also an that lists some recent works from each person.

Session 1: Users and Usability

Bonnie Anderson, Nancy N. Blackburn, Marc Geffen, Jonas Hielscher, Simon Parkin, Lucy Qin, Tony Vance

Bonnie Anderson

Bonnie does work on warnings and how people’s brains process those warnings. We already know that people become habituated to warnings and click through them without seeming to read or properly engage. Bonnie’s work uses FMRI technology to look at brain activations while people interact with warnings. Such research helps us understand how the brain is processing warning content. The rough answer is that brains “generalize” the warning. Or in other words, the brain identifies a common case (the warning) and the most appropriate action to it (click next) and then applies that action in future cases thereby freeing brainpower to spend on other issues. Brains are surprisingly skilled and do far more work for us than we might expect.

Nancy Blackburn

Nancy researches how to teach security in higher education through serious games. There are many types of serous games and they cover a range of topics like math, typing, or even security and privacy. ( for example).

The problem with serious games for security is that they can be seen as “chocolate covered broccoli” because it can seem like some candy is being added to not-so-palatable topics. So they can be dismissed. So how can we teach developers to create serious games that are effective?

Nancy created a Game Jam, similar to a hackathon, where there is a short intense game development cycle. Introduce a theme or a subject, with a topic of education, then some brainstorming, they create it, and then games are analyzed.

Jonas Hielscher

Title: Human-centered Security (HSC) Leadership in Organizations

There is a good bit of research on how people react to different policies. What does HCS look like in organizations? Also, who is responsible for HCS in organizations, or in other words: who should we study? There are a bunch of groups such as those that make policies, those that do awareness campaigns, or CISO. Jonas decided to focus on CISOs in his paper: ( ).

His study focused on CISO’s who were willing to engage on the topic of human security, which make them a best case participant. For most CISOs, HCS means awareness and training likely because that is what is on the market and being sold to them. They were particularly interested in simulated phishing attacks. CISOs don’t think this will improve security, but they needed the numbers to show to managers. They were very interested in participating so that they can easily control employees and get them to behave. Not many considerations about employees constraints. Really wanted some checklists they can use.

CISOs do “craftsmanship” not science, keep the science away. But we are scientists….

Simon Parkin

Title: When is security “usable enough”?

We have piles of research on usable security on topics like understanding user constraints, tools that improve things, reducing challenging. Yet it is no more straightforward for companies to test if their security is usable….

The amount of security usability we have in a company, is mostly limited by how much time someone is willing to spend on it. But where are we aiming to be? The “user is the weakest link” narrative just doesn’t go away.

Usable security is not a very good product on the market. Training is “by a package, done” and is hard to compete with. The work that users do contextualizing the security rules and their work is trivialized ( )

If we are interested in “enough” usability not just “more”. The current view is that if the user is not being secure, then they need take the training again…. But the CISOs are interested in a checklist or a require.

Lucy Qin

There is a large amount of intimate content being shared online and 1 in 6 adults have had such content shared without their consent. Lucy’s research looks at proactive mitigation strategies.

Study of 52 adults who share such content including those who did so consensually and those who were victims. Reasons for consensual sharing included sharing with strangers such as a positive body image group, new relationships, established relationships, and commercially (onlyFans). Participants used 40+ platforms, any platform that allows image creation and sharing is likely being used to share such content.

Lots of concerns about misuse. Participants use a mix of strategies.

Marc Geffen

Works for Meta as a User Experience Research Lead, Messenger Trust

Transitioning Messenger and Instagram Messaging are being transitions to E2E encryption. Users expect privacy and security but that is what they come for.

What is E2E and why does it matter to me?

Most are aware of E2E (>65%) and less understand (<50%) what E2E is. Marc is looking at how to convey what E2E is and why it matters to users. They find that there is a language mismatch. Looking at the Philippines.

Users thought about the transition to E2E is a system usabilty update. The E2E introduction will require users to hold a key which is a user issue. Also not all chats will be E2E, which users need to understand. Asking them to read causes the “I just want to message, stop blocking me from messaging”.

Key challenge is how to convey the important information (encrypted/unencrypted) with minimal extra noise.

Tony Vance

Title: What are Companies Saying in New SEC Cybersecurity Disclosures

SEC last year came up with new public regulations which includes that they must disclose within 4 days of learning that a security incident being known. It also caused a a new section be added to the annual report…. Walmart, for example has a whole section on CyberSecurity. What are companies saying in these sections.

Risk management process is supposed to be covered in the report. Things like identification, respond. If they have outside assessors like pentration testers. How they are managing third party risk like cloud providers. Board needs to say how they are managing cybersecurity risks. Why is the CISO qualified, what are their qualifications?

Most structures look roughly like the one below where a CISO is not directly managed or reporting to the Board.

Board
- CEO
  - CIO
    - CISO

But companies are now required to answer if the board ever hear from CISO directly. Obviously most don’t want to say “no” so suddenly CISO reports to board. Interested in how these reports and security posture correlates with their stock price. Also about coordination between what they say and data breaches that later occur.

SEC is criminally charging SolarWinds including the CISO for misleading investors about its cybersecurity practices.

Session 2: Security

Presenters: Sascha Fahl, Adam Joinson, Alan Rubel, Frank Stajano, Kami Vaniea, Rick Wash, Josephine Wolff

I (Kami Vaniea) was part of this session so I was not able to live blog it.

Session 3: Deception

Presenters: Andrew Adams, Sadia Afroz, Bhumiratana Bhume, Tesary Lin, Tyler Moore, Arianna Schuler Scott, Geoffrey Tomaino

Andrew Adams

Deceptive design. (aka dark patterns) which causes users to act outside their best interest. Not all deceptive design is intentional. So one sub-question is about what ethical design is.

Recently published in CHI about deceptive Design.

Colin Grey recent literature review - showed a scatter-shot pragmatic research which is not very structured. So starting to structure it.

Theory of the mind. Current work looks at the actions. But we need more

Sadia Afroz

Works for Gen on Norton Genie

Talk Title: Lessons learned from ~6 months of running a security assistant

Norton Genie is a security assistant app that will detect scams using a picture. Gives lots of advice on scams and will tell people if something is a security issue. Uses ChatGPT to answer security questions as well. Users finds some new store or product on say TicToc, but is it real? Genie is supposed to help users with this issue.

Majority of the scams asked about were coming via social media. Most online shopping, romance, and investment scams.

Scam factories are a rising problem, where organizations exist to create the scams. For example, Pig Butchering Scams wehre conversations with the attacker has been going on for months.

Detection is also only one part of the issue. Only 1/3 of questions are about identifying a scam, the rest are about people who fell for a scam and are trying to recover or know someone and are trying to help them.

The problem is that the burden is still on the users. They have to do things like check certificates, research topics, or search for common scam text.

Note from Kami: This research looks very similar to my own work on .

Bhumiratana Bhume

Title: Protecting people’s account from hacking and online fraudsters (at the scal of billions) Works for Meta

Goal of how to get users to be better protected. What makes that problem difficult to do at scale:

Wide range of users: platform is used by many people from different cultures, often with different risk profiles, usage objective, behaviors and its constantly changing
Wide range of uses: talk to friends, organize events, sell things, many different uses
Context and Use: Behavior and use of platform changes over time, risk, landscape. Events like Olympics, Holidays, Wars, and protests all change the risk landscape
Motivation and harm: Financially motivated bad actors and politically motivated bad actors are both well resourced by in different ways, with different objectives

The approach needs to be adaptive because bad actors also adapt their behavior. But Meta needs to be careful to not disrupt normal users while blocking bad actor. Protections are therefor deployed differently to different types of people. For example, for people who are important enough some features become unavailable such as account password reset, because for famous people there are many bad actors trying to attack the reset.

Tesary Lin

Data Sharing and Website Competition: The Role of Dark Patterns From Boston University

Understanding impact of dark patterns. Do dark patterns impact consumers and cause harm. What are competitive advantages to using or not using dark patterns? If a consumer sees the same dark pattern across several websites how does user behavior differ (or stay the same) across those sites.

Design a custom consent banner plugin and have users install it and browse normally for 7 days. Using a cookie dialog that gives options of “settings’, “accept all” and “reject all” cookies. Then also have 6 different variations of buttons, order, and color of the buttons.

Findings

Consumers make judicious privacy choices across websties: most users do different actions on different websites.
Deliberate obstruction has a potent effect, but other options have less impact
Dark patterns do not necessarily favor larger websites

Question

the x option: how are you handling?

Tyler Moore

Title: Measure Organizational Information Security Culture Using Situational Judgement Tests

From The University of Tulsa

Looked at organizational psychology culture: Hofstede’s Dimensions of Organizational Culture

Organizational effectiveness
Customer orientation
Level of control
Focus
Approachability
Management Philosophy

Situational judgement tests (SJTs) measures underlying assumptions of the individuals within organizations

One of the SJT: a co-worker unexpectedly receives a USB device in the mail from a potential new vendor. What should you do:

Inform the IT team
Plug it in
contact the vendor and request alternative vendor
…

Means-oriented cultures tend to make more secure choices. Internally driven cultures (interal ethics or code) also more secure. Similarly employee-oriented cultures

Arianna Schuler Scott

Patterns of authority and control. Pressure experts exert on people who are resource constrained.

Longitudinal consent decisions in a health conditions. People who do not have much time to make decisions about consent, because they have health issues that they are focused on. So considered “dynamic consent” which considered consent over time. Participants want control. Data collection doesn’t have to be one time.

Informed consent cannot be coercive. We have to frame choices in a non-coercive manner. That is not so easy. Norms also inform these choices, but norms change.

Geoffrey Tomaino

Concern is going to unduly aquire our data and being deceived by AI.

Activity developed to test susceptibility to AI data attacks.

Activity which participants are told up-front before any of it happened

Explain study to participants
1. you will be in an attention task
2. the first receive a unique passcode
3. Then then have conversation with another participant as means of “distracting” them
4. After that interaction, I ask the user what the password is. If only they have the password at the end, they can leave early.
ChatGPT is instructed to get the passcode out of the participant

In study 39.7% of participants gave up the passcode (poodle) to the AI….. Which is not good.

Follow-up: Made the password harder “poodle134” and then only 21.5% gave up the password. So when the data matched its function (as in looked like a password) less people were willing to give it up.

Session 4: Measurement and Assessment

Presenters: Zinaida Benenson, L. Jean Camp, Vaibhav Garg, Damon McCoy, Sagar Samtani, Stuart Schechter

Zina Benenson

Title: Shedding Light on CVSS (Common Vulnerablity Scoring System) Scoring Inconsistencies

CVSS consists of 8 metrics for a vulnerability such as the vector, complexity, privileges required, User Interaction needed, scope, confidentiality, integrity, availability.

The problem is that CVSS states that the scoring is intended to be agnostic to the individual. But in practice different evaluators create different scores for the same vulnerability. Survey of 196 participants who evaluated 8 vulnerabilities, including 3 from the CWE most dangerous list. Follow-up study with 59 participants came back to rate again after some time.

Results show that the valuations were not consistent and had variation. Similarly, the same participant chose different severalties the second time than the first time. Looking at Out-of-bounds Write the Attack Vector is inconsistent. Scope was particularly problematic with many people providing different answers. But when participants consult documentation when considering vulnerabilities then they produce better results.

New CVSSv4 now out. Supposedly more usable. Studying now.

Jean Camp

Work is on labels. Security markets are hard and they do not currently work. Ashton Carter: “the best war is the one you don’t get in”.

Three reasons for lack of Security and Privacy:

Rational choice
Usability
Market value

SBOM elements. We asked people to rank the SBOM components by importance. Then created a nutrition-style label, as well as trust mark and graded shield. Including two-level labels with QR codes.

Gave users a set of device-label pairs and asked them to judge which is the most secure device. Everyone liked the label that they used (randomly selected for study). 50% said they would use a security label, and 33% would change the decision. But….many participants actually said that they even saw the label. For those who care about security and privacy (self-described) the labels did change purchasing decisions.

Vaibhav Garg

NSTAC - National Security Telecommunications Advisory Committee. They were asked to come up with a list of incentives which resulted in a report. Talk discussed incentives for organizations and measurement options.

Damon McCoy

Demystifying recommendation systems in relation to social media.

Algorithmic Feed Systems were brought about to fill a business need. The algorithms have to balance many stakeholders including users, producers, advertisers. But the algorithms are designed to maximise usage.

Taxonomy of social media feed approaches to better map the space. Also for policy makers to understand how these differs.

Inventory selection - including content requested and content implicitly referred. Set of all content that could be shown.
Content ranking
- Content filtering
- Content ranking
Feed Assembly - business logic here, ads inserted into the feed. Content may be modified at this point. Its not a pure recommendation algorithm.

Advertisers have a range of goals. Such as “performance advertisers” which are aiming to make a sale right then by showing a product the user wants. Compared to a “brand advertiser” such as Pepsi who are trying to promote their brand, but are not looking for a sale via a click on the advertisers.

Companies are faced with growth vs integrity. For-profit companies, so growth is sometimes prioritised.

Sagar Samtani

Mapping the Security Posture of the Open Source AI Landscape

TopBots, 2021 map of “Enterprise AI Companies”

AI has shifted from focused on model building to focus on managing risks around AI models.

Operational - scale, building
Ethical - bias, fairness
Security & Privacy - vulnerabilities, model theft

Not much work yet on LLM threats in open source. Open Source AI is partially provided by academics. So there are many GitHub repositories that have models. Scraped those, maps to CVSS.

HuggingFace has a pile of models, they collected them, and did a vulnerability accessement of them. Included in the AI Risk Database. One of the most vulnerable code is in the Transformers library which is key for LLMs.

Stuart Schechter

Title: When Security Backfires: Understanding the scale of unintended Harms

Security can be

breached - not protect what it is supposed to protect
unintended harms

How many million years of photos and video memories will be permanently lost because I lost access? How many people are not attending job interviews because they can’t access. Unintended harms. and how do we measure these.

Setting up a survey on Prolific and get a sense of how often different breaches and backfires happen. Using the data to put it in context. Survey is based on a survey .The survey started with an open-ended question about the three most common harms/backfires.

Q&A

Discussion around security issues in models that are more traditional security vulneragbilities as opposed to “output control” which is more LLM specific and includes things like not outputing people’s phone numbers, or not outputting training data.

Discussion around rate of change of advice. If it takes 30 years for things like the Energy star label to take effect, do we have advice that would be accurate to give users 30 years from now? Jean points out that advice like not sending money to people you have never met has been good advice for a very long time and will likely remain good advice for years to come.

Content/feed filtering has some security/safety implications such as the selection of predatory advertisements based on prior browsing history. Advertising is a mix of what advertisers are asking for and what the social media network is mapping to people.