I’ll be trying to liveblog the seventeenth Workshop on the Economics of Information Security (WEIS), which is being held online today and tomorrow (December 14/15) and streamed live on the CEPS channel on YouTube. The event was introduced by the general chair, Lorenzo Pupillo of CEPS, and the program chair Nicolas Christin of CMU. My summaries of the sessions will appear as followups to this post, and videos will be linked here in a few days.
How far can we go with acoustic snooping on data?
Seven years ago we showed that you could use a phone camera to measure the phone’s motion while typing and use that to recover PINs. Four years ago we showed that you could use interrupt timing to recover text entered using gesture typing. Last year we showed how a gaming app can steal your banking PIN by listening to the vibration of the screen as your finger taps it. In that attack we used the on-phone microphones, as they are conveniently located next to the screen and can hear the reverberations of the screen glass.
This year we wondered whether voice assistants can hear the same taps on a nearby phone as the on-phone microphones could. We knew that voice assistants could do acoustic snooping on nearby physical keyboards, but everyone had assumed that virtual keyboards were so quiet as to be invulnerable.
Almos Zarandy, Ilia Shumailov and I discovered that attacks are indeed possible. In Hey Alexa what did I just type? we show that when sitting up to half a meter away, a voice assistant can still hear the taps you make on your phone, even in presence of noise. Modern voice assistants have two to seven microphones, so they can do directional localisation, just as human ears do, but with greater sensitivity. We assess the risk and show that a lot more work is needed to understand the privacy implications of the always-on microphones that are increasingly infesting our work spaces and our homes.
This is a guest post by Alex Shepherd.
There is a growing body of research literature concerning the potential threat of physical-world adversarial attacks against machine-vision models. By applying adversarial perturbations to physical objects, machine-vision models may be vulnerable to images containing these perturbed objects, resulting in an increased risk of misclassification. The potential impacts could be significant and have been identified as risk areas for autonomous vehicles and military UAVs.
For this Three Paper Thursday, we examine the following papers exploring the potential threat of physical-world adversarial attacks, with a focus on the impact for autonomous vehicles.
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. Adversarial examples in the physical world, arXiv:1607.02533 (2016)
In this seminal paper, Kurakin et al. report their findings of an experiment conducted using adversarial images taken from a phone camera as input for a pre-trained ImageNet Inceptionv3 image classification model. Methodology was based on a white-box threat model, with adversarial images crafted from the ImageNet validation dataset using the Inceptionv3 model.
Continue reading Three Paper Thursday: Attacking Machine Vision Models In Real Life
The SHB seminar on November 5th was kicked off by Tom Holt, who’s discovered a robust underground market in identity documents that are counterfeit or fraudulently obtained. He’s been scraping both websites and darkweb sites for data and analysing how people go about finding, procuring and using such credentials. Most vendors were single-person operators although many operate within affiliate programs; many transactions involved cryptocurrency; many involve generating pdfs that people can print at home and that are good enough for young people to drink alcohol. Curiously, open web products seem to cost twice as much as dark web products.
Next was Jack Hughes, who has been studying the contract system introduced by hackforums in 2018 and made mandatory the following year. This enabled him to analyse crime forum behaviour before and during the covid-19 era. How do new users become active, and build up trust? How does it evolve? He collected 200,000 transactions and analysed them. The contract mandate stifled growth quickly, leading to a first peak; covid caused a second. The market was already centralised, and became more so with the pandemic. However contracts are getting done faster, and the main activity is currency exchange: it seems to be working as a cash-out market.
Anita Lavorgna has been studying the discourse of groups who oppose public mask mandates. Like the antivaxx movement, this can draw in fringe groups and become a public-health issue. She collected 23654 tweets from February to June 2020. There’s a diverse range of voices from different places on the political spectrum but with a transversal theme of freedom from government interference. Groups seek strength in numbers and seek to ally into movements, leading to the mask becoming a symbol of political identity construction. Anita found very little interaction between the different groups: only 144 messages in total.
Simon Parkin has been working on how we can push back on bad behaviours online while they are linked with good behaviours that we wish to promote. Precision is hard as many of the desirable behaviours are not explicitly recognised as such, and as many behaviours arise as a combination of personal incentives and context. The best way forward is around usability engineering – making the desired behaviours easier.
Bruce Schneier was the final initial speaker, and his topic was covid apps. The initial rush of apps that arrived in March through June have known issues around false positives and false negatives. We’ve also used all sorts of other tools, such as analysis of Google maps to measure lockdown compliance. The third thing is the idea of an immunity passport, saying you’ve had the disease, or a vaccine. That will have the same issues as the fake IDs that Tom talked about. Finally, there’s compliance tracking, where your phone monitors you. The usual countermeasures apply: consent, minimisation, infosec, etc., though the trade-offs might be different for a while. A further bunch of issues concern home working and the larger attack surface that many firms have as a result of unfamiliar tools, less resistance to being tols to do things etc.
The discussion started on fake ID; Tom hasn’t yet done test purchases, and might look at fraudulently obtained documents in the future, as opposed to completely counterfeit ones. Is hackforums helping drug gangs turn paper into coin? This is not clear; more is around cashing out cybercrime rather than street crime. There followed discussion by Anita of how to analyse corpora of tweets, and the implications for policy in real life. Things are made more difficult by the fact that discussions drift off into other platforms we don’t monitor. Another topic was the interaction of fashion: where some people wear masks or not as a political statement, many more buy masks that get across a more targeted statement. Fashion is really powerful, and tends to be overlooked by people in our field. Usability research perhaps focuses too much on the utilitarian economics, and is a bit of a blunt instrument. Another example related to covid is the growing push for monitoring software on employees’ home computers. Unfortunately Uber and Lyft bought a referendum result that enables them to not treat their staff in California as employees, so the regulation of working hours at home will probably fall to the EU. Can we perhaps make some input into what that should look like? Another issue with the pandemic is the effect on information security markets: why should people buy corporate firewalls when their staff are all over the place? And to what extent will some of these changes be permanent, if people work from home more? Another thread of discussion was how the privacy properties of covid apps make it hard for people to make risk-management decisions. The apps appear ineffective because they were designed to do privacy rather than to do public health, in various subtle ways; giving people low-grade warnings which do not require any action appear to be an attempt to raise public awareness, like mask mandates, rather than an effective attempt to get exposed individuals to isolate. Apps that check people into venues have their own issues and appear to be largely security theatre. Security theatre comes into its own where the perceived risk is much greater than the actual risk; covid is the opposite. What can be done in this case? Targeted warnings? Humour? What might happen when fatigue sets in? People will compromise compliance to make their lives bearable. That can be managed to some extent in institutions like universities, but in society it will be harder. We ended up with the suggestion that the next SHB seminar should be in February, which should be the low point; after that we can look forward to things getting better, and hopefully to a meeting in person in Cambridge on June 3-4 2021.
Our beloved Vice-Chancellor proposes a “free speech” policy under which all academics must treat other academics with “respect”. This is no doubt meant well, but the drafting is surprisingly vague and authoritarian for a university where the VC, the senior pro-VC, the HR pro-VC and the Registrary are all lawyers. The bottom line is that in future we might face disciplinary charges and even dismissal for mockery of ideas and individuals with which we disagree.
The policy was slipped out in March, when nobody was paying attention. There was a Discussion in June, at which my colleague Arif Ahmad spelled out the problems.
Vigorous debate is intrinsic to academia and it should be civil, but it is unreasonable to expect people to treat all opposing views with respect. Oxford’s policy spells this out. At the Discussion, Arif pointed out that “respect” must be changed to “tolerance” if we are to uphold the liberal culture that we have not just embraced but developed over several centuries.
At its first meeting this term, the University Council considered these arguments but decided to press ahead anyway. We are therefore calling a ballot on three amendments to the policy. If you’re a senior member of the University we invite you to sign up your support for them on the flysheets. The first amendment changes “respect” to “tolerance”; the second makes it harder to force university societies to disinvite speakers whose remarks may be controversial, and the third restricts the circumstances in which the university itself can ban speakers.
Liberalism is coming under attack from authoritarians of both left and right, yet it is the foundation on which modern academic life is built and our own university has contributed more than any other to its development over the past 811 years. If academics can face discipline for using tactics such as scorn, ridicule and irony to criticise folly, how does that sit with having such alumni as John Maynard Keynes and Charles Darwin, not to mention Bertrand Rusell, Douglas Adams and Salman Rushdie?
This is a guest post by Cassandra Cross.
Romance fraud (also known as romance scams or sweetheart swindles) affects millions of individuals globally each year. In 2019, the Internet Crime Complaint Centre (IC3) (USA) had over US$475 million reported lost to romance fraud. Similarly, in Australia, victims reported losing over $AUD80 million and British citizens reported over £50 million lost in 2018. Given the known under-reporting of fraud overall, and online fraud more specifically, these figures are likely to only be a minority of actual losses incurred.
Romance fraud occurs when an offender uses the guise of a legitimate relationship to gain a financial advantage from their victim. It differs from a bad relationship, in that from the outset, the offender is using lies and deception to obtain monetary rewards from their partner. Romance fraud capitalises on the fact that a potential victim is looking to establish a relationship and exhibits an express desire to connect with someone. Offenders use this to initiate a connection and start to build strong levels of trust and rapport.
As with all fraud, victims experience a wide range of impacts in the aftermath of victimisation. While many believe these to be only financial, in reality, it extends to a decline in both physical and emotional wellbeing, relationship breakdown, unemployment, homelessness, and in extreme cases, suicide. In the case of romance fraud, there is the additional trauma associated with grieving both the loss of the relationship as well as any funds they have transferred. For many victims, the loss of the relationship can be harder to cope with than the monetary aspect, with victims experiencing large degrees of betrayal and violation at the hands of their offender.
Sadly, there is also a large amount of victim blaming that exists with both romance fraud and fraud in general. Fraud is unique in that victims actively participate in the offence, through the transfer of money, albeit under false pretences. As a result, they are seen to be culpable for what occurs and are often blamed for their own circumstances. The stereotype of fraud victims as greedy, gullible and naïve persists, and presents as a barrier to disclosure as well as inhibiting their ability to report the incident and access any support services.
Given the magnitude of losses and impacts on romance fraud victims, there is an emerging body of scholarship that seeks to better understand the ways in which offenders are able to successfully target victims, the ways in which they are able to perpetrate their offences, and the impacts of victimisation on the individuals themselves. The following three articles each explore different aspects of romance fraud, to gain a more holistic understanding of this crime type.
I’ll be liveblogging the workshop on security and human behaviour, which is online this year. My liveblogs will appear as followups to this post. This year my program co-chair is Alice Hutchings and we have invited a number of eminent criminologists to join us. Edited to add: here are the videos of the sessions.
Deep neural networks (DNNs) have been a very active field of research for eight years now, and for the last five we’ve seen a steady stream of adversarial examples – inputs that will bamboozle a DNN so that it thinks a 30mph speed limit sign is a 60 instead, and even magic spectacles to make a DNN get the wearer’s gender wrong.
So far, these attacks have targeted the integrity or confidentiality of machine-learning systems. Can we do anything about availability?
Sponge Examples: Energy-Latency Attacks on Neural Networks shows how to find adversarial examples that cause a DNN to burn more energy, take more time, or both. They affect a wide range of DNN applications, from image recognition to natural language processing (NLP). Adversaries might use these examples for all sorts of mischief – from draining mobile phone batteries, though degrading the machine-vision systems on which self-driving cars rely, to jamming cognitive radar.
So far, our most spectacular results are against NLP systems. By feeding them confusing inputs we can slow them down over 100 times. There are already examples in the real world where people pause or stumble when asked hard questions but we now have a dependable method for generating such examples automatically and at scale. We can also neutralize the performance improvements of accelerators for computer vision tasks, and make them operate on their worst case performance.
One implication is that engineers designing real-time systems that use machine learning will have to pay more attention to worst-case behaviour; another is that when custom chips used to accelerate neural network computations use optimisations that increase the gap between worst-case and average-case outcomes, you’d better pay even more attention.
Yesterday’s publication of the minutes of the government’s Scientific Advisory Group for Emergencies (SAGE) raises some interesting questions. An initial summary in yesterday’s Guardian has a timeline suggesting that it was the distinguished medics on SAGE rather than the Prime Minister who went from complacency in January and February to panic in March, and who ignored the risk to care homes until it was too late.
Is this a Machiavellian conspiracy by Dominic Cummings to blame the scientists, or is it business as usual? Having spent a dozen years on the university’s governing body and various of its subcommittees, I can absolutely get how this happened. Once a committee gets going, it can become very reluctant to change its opinion on anything. Committees can become sociopathic, worrying about their status, ducking liability, and finding reasons why problems are either somebody else’s or not practically soluble.
So I spent a couple of hours yesterday reading the minutes, and indeed we see the group worried about its power: on February 13th it wants the messaging to emphasise that official advice is both efficaceous and sufficient, to “reduce the likelihood of the public adopting unnecessary or contradictory behaviours”. Turf is defended: Public Health England (PHE) ruled on February 18th that it can cope with 5 new cases a week (meaning tracing 800 contacts) and hoped this might be increased to 50; they’d already decided the previous week that it wasn’t possible to accelerate diagnostic capacity. So far, so much as one might expect.
The big question, though, is why nobody thought of protecting people in care homes. The answer seems to be that SAGE dismissed the problem early on as “too hard” or “not our problem”. On March 5th they note that social distancing for over-65s could save a lot of lives and would be most effective for those living independently: but it would be “a challenge to implement this measure in communal settings such as care homes”. They appear more concerned that “Many of the proposed measures will be easier to implement for those on higher incomes” and the focus is on getting PHE to draft guidance. (This is the meeting at which Dominic Cummings makes his first appearance, so he cannot dump all the blame on the scientists.)
This is a guest contribution from Daniel Woods.
This coming Monday will mark two years since the General Data Protection Regulation (GDPR) came into effect. It prompted an initial wave of cookie banners that drowned users in assertions like “We value your privacy”. Website owners hoped that collecting user consent would ensure compliance and ward off the lofty fines.
Article 6 of the GDPR describes how organisations can establish a legal basis for processing personal data. Putting aside a selection of `necessary’ reasons for doing so, data processing can only be justified by collecting the user’s consent to “the processing of his or her personal data for one or more specific purposes”. Consequently, obtaining user consent could be the difference between suffering a dizzying fine or not.
The law changed the face of the web and this post considers one aspect of the transition. Consent Management Providers (CMPs) emerged offering solutions for websites to embed. Many of these use a technical standard described in the Transparency and Consent Framework. The standard was developed by the Industry Advertising Body, who proudly claim it is is “the only GDPR consent solution built by the industry for the industry”.
All of the following studies either directly measure websites implementing this standard or explore the theoretical implications of standardising consent. The first paper looks at how the design of consent dialogues shape the consent signal sent by users. The second paper identifies disparities between the privacy preferences communicated via cookie banners and the consent signals stored by the website. The third paper uses coalitional game theory to explore which firms extract the value from consent coalitions in which websites share consent signals.