Category Archives: Legal issues

Security-related legislation, government initiatives, court cases

Bugs in our pockets?

In August, Apple announced a system to check all our iPhones for illegal images, then delayed its launch after widespread pushback. Yet some governments continue to press for just such a surveillance system, and the EU is due to announce a new child protection law at the start of December.

Now, in Bugs in our Pockets: The Risks of Client-Side Scanning, colleagues and I take a long hard look at the options for mass surveillance via software embedded in people’s devices, as opposed to the current practice of monitoring our communications. Client-side scanning, as the agencies’ new wet dream is called, has a range of possible missions. While Apple and the FBI talked about finding still images of sex abuse, the EU was talking last year about videos and text too, and of targeting terrorism once the argument had been won on child protection. It can also use a number of possible technologies; in addition to the perceptual hash functions in the Apple proposal, there’s talk of machine-learning models. And, as a leaked EU internal report made clear, the preferred outcome for governments may be a mix of client-side and server-side scanning.

In our report, we provide a detailed analysis of scanning capabilities at both the client and the server, the trade-offs between false positives and false negatives, and the side effects – such as the ways in which adding scanning systems to citizens’ devices will open them up to new types of attack.

We did not set out to praise Apple’s proposal, but we ended up concluding that it was probably about the best that could be done. Even so, it did not come close to providing a system that a rational person might consider trustworthy.

Even if the engineering on the phone were perfect, a scanner brings within the user’s trust perimeter all those involved in targeting it – in deciding which photos go on the naughty list, or how to train any machine-learning models that riffle through your texts or watch your videos. Even if it starts out trained on images of child abuse that all agree are illegal, it’s easy for both insiders and outsiders to manipulate images to create both false negatives and false positives. The more we look at the detail, the less attractive such a system becomes. The measures required to limit the obvious abuses so constrain the design space that you end up with something that could not be very effective as a policing tool; and if the European institutions were to mandate its use – and there have already been some legislative skirmishes – they would open up their citizens to quite a range of avoidable harms. And that’s before you stop to remember that the European Court of Justice struck down the Data Retention Directive on the grounds that such bulk surveillance, without warrant or suspicion, was a grossly disproportionate infringement on privacy, even in the fight against terrorism. A client-side scanning mandate would invite the same fate.

But ‘if you build it, they will come’. If device vendors are compelled to install remote surveillance, the demands will start to roll in. Who could possibly be so cold-hearted as to argue against the system being extended to search for missing children? Then President Xi will want to know who has photos of the Dalai Lama, or of men standing in front of tanks; and copyright lawyers will get court orders blocking whatever they claim infringes their clients’ rights. Our phones, which have grown into extensions of our intimate private space, will be ours no more; they will be private no more; and we will all be less secure.

Is Apple’s NeuralMatch searching for abuse, or for people?

Apple stunned the tech industry on Thursday by announcing that the next version of iOS and macOS will contain a neural network to scan photos for sex abuse. Each photo will get an encrypted ‘safety voucher’ saying whether or not it’s suspect, and if more than about ten suspect photos are backed up to iCloud, then a clever cryptographic scheme will unlock the keys used to encrypt them. Apple staff or contractors can then look at the suspect photos and report them.

We’re told that the neural network was trained on 200,000 images of child sex abuse provided by the US National Center for Missing and Exploited Children. Neural networks are good at spotting images “similar” to those in their training set, and people unfamiliar with machine learning may assume that Apple’s network will recognise criminal acts. The police might even be happy if it recognises a sofa on which a number of acts took place. (You might be less happy, if you own a similar sofa.) Then again, it might learn to recognise naked children, and flag up a snap of your three-year-old child on the beach. So what the new software in your iPhone actually recognises is really important.

Now the neural network described in Apple’s documentation appears very similar to the networks used in face recognition (hat tip to Nicko van Someren for spotting this). So it seems a fair bet that the new software will recognise people whose faces appear in the abuse dataset on which it was trained.

So what will happen when someone’s iPhone flags ten pictures as suspect, and the Apple contractor who looks at them sees an adult with their clothes on? There’s a real chance that they’re either a criminal or a witness, so they’ll have to be reported to the police. In the case of a survivor who was victimised ten or twenty years ago, and whose pictures still circulate in the underground, this could mean traumatic secondary victimisation. It might even be their twin sibling, or a genuine false positive in the form of someone who just looks very much like them. What processes will Apple use to manage this? Not all US police forces are known for their sensitivity, particularly towards minority suspects.

But that’s just the beginning. Apple’s algorithm, NeuralMatch, stores a fingerprint of each image in its training set as a short string called a NeuralHash, so new pictures can easily be added to the list. Once the tech is built into your iPhone, your MacBook and your Apple Watch, and can scan billions of photos a day, there will be pressure to use it for other purposes. The other part of NCMEC’s mission is missing children. Can Apple resist demands to help find runaways? Could Tim Cook possibly be so cold-hearted as to refuse at add Madeleine McCann to the watch list?

After that, your guess is as good as mine. Depending on where you are, you might find your photos scanned for dissidents, religious leaders or the FBI’s most wanted. It also reminds me of the Rasterfahndung in 1970s Germany – the dragnet search of all digital data in the country for clues to the Baader-Meinhof gang. Only now it can be done at scale, and not just for the most serious crimes either.

Finally, there’s adversarial machine learning. Neural networks are fairly easy to fool in that an adversary can tweak images so they’re misclassified. Expect to see pictures of cats (and of Tim Cook) that get flagged as abuse, and gangs finding ways to get real abuse past the system. Apple’s new tech may end up being a distributed person-search machine, rather than a sex-abuse prevention machine.

Such a technology requires public scrutiny, and as the possession of child sex abuse images is a strict-liability offence, academics cannot work with them. While the crooks will dig out NeuralMatch from their devices and play with it, we cannot. It is possible in theory for Apple to get NeuralMatch to ignore faces; for example, it could blur all the faces in the training data, as Google does for photos in Street View. But they haven’t claimed they did that, and if they did, how could we check? Apple should therefore publish full details of NeuralMatch plus a set of NeuralHash values trained on a public dataset with which we can legally work. It also needs to explain how the system it deploys was tuned and tested; and how dragnet searches of people’s photo libraries will be restricted to those conducted by court order so that they are proportionate, necessary and in accordance with the law. If that cannot be done, the technology must be abandoned.

Patient confidentiality in remote consultations

During the lockdown last year, I was asked by the International Psychoanalytic Association (IPA) to help them update their guidance on remote consultations. I spoke to a range of GPs, surgeons, psychologists and psychoanalysts about what they’d learned during the first lockdown about working over the phone, or over Skype or Zoom. The IPA has now published my report, on a web page that also has their guidance to members both before and after the exercise.

Before the pandemic, remote consultation did happen, but not all therapists offered it; and confidentiality concerns tended to focus on technical security measures such as whether the call was encrypted end-to-end. After everyone was forced online in March and April 2020, clinicians learned rapidly to focus on the endpoints. Patients often have problems finding a private space to talk; there may be a family member in earshot, whether by accident, or because they’re cooped up in a tiny apartment, or because they have a controlling partner or parent. A clinician may return a patient’s call and catch them in a supermarket queue. And the clinic too can be interrupted, if the clinician is practicing from home.

Technical endpoint compromise is occasionally an issue; a controlling family member could inspect a patient’s device and discover a therapeutic relationship that had not been disclosed. By far the worst endpoint compromise that happened during the study period was when the Vastaamo chain of clinics in Finland was hit by ransomware; 45,000 patients’ records were stolen, and some were put online by extortionists demanding bitcoin payments. (And now we face an even larger-scale issue in the UK as the government plans to hoover up all our GP records for sale to drug companies unless we opt out by June 25; see here for how to do that.)

Such horrors aside, the core problem is to establish a therapeutic space where both patient and clinician can interact effectively, which means being able to concentrate and also to relax. There’s more to this than just being comfortable trusting the endpoint environments, the devices, the communications medium and any record-keeping mechanism. Interaction matters too. Many clinician communities discovered independently that the plain old telephone system often works better than new-fangled stuff such as skype and zoom. Video calls add maybe half a second of latency for buffering, which destroys conversational turn-taking. A further advantage of the phone is that you’re not staring at someone’s face at an unnatural distance. You can walk around the room, or even walk around the park.

Since doing this work I’ve started to avoid zoom and teams in favour of phone calls when I can, and use end-to-end encrypted voice calls on WhatsApp or Signal where call costs or client confidentiality make it sensible.

Robots, manners and stress

Humans and other animals have evolved to be aware of whether we’re under threat. When we’re on safe territory with family and friends we relax, but when we sense that a rival or a predator might be nearby, our fight-or-flight response kicks in. Situational awareness is vital, as it’s just too stressful to be alert all the time.

We’ve started to realise that this is likely to be just as important in many machine-learning applications. Take as an example machine vision in an automatic driver assistance system, whose goal is automatic lane keeping and automatic emergency braking. Such systems use deep neural networks, as they perform way better than the alternatives; but they can be easily fooled by adversarial examples. Should we worry? Sure, a bad person might cause a car crash by projecting a misleading image on a motorway bridge – but they could as easily steal some traffic cones from the road works. Nobody sits up at night worrying about that. But the car industry does actually detune vision systems from fear of deceptive attacks!

We therefore started a thread of research aimed at helping machine-learning systems detect whether they’re under attack. Our first idea was the Taboo Trap. You raise your kids to observe social taboos – to behave well and speak properly – and yet once you send them to school they suddenly know words that would make your granny blush. The taboo violation shows they’ve been exposed to ‘adversarial inputs’, as an ML engineer would call them. So we worked out how to train a neural network to avoid certain taboo values, both of outputs (forbidden utterances) and intermediate activations (forbidden thoughts). The taboos can be changed every time you retrain the network, giving the equivalent of a cryptographic key. Thus even though adversarial samples will always exist, you can make them harder to find; an attacker can’t just find one that works against one model of car and use it against every other model. You can take a view, based on risk, of how many different keys you need.

We then showed how you can also attack the availability of neural networks using sponge examples – inputs designed to soak up as much energy, and waste as much time, as possible. An alarm can be simpler to build in this case: just monitor how long your classifier takes to run.

Are there broader lessons? We suspect so. As robots develop situational awareness, like humans, and react to real or potential attacks by falling back to a more cautious mode of operation, a hostile environment will cause the equivalent of stress. Sometimes this will be deliberate; one can imagine constant low-level engagement between drones at tense national borders, just as countries currently probe each others’ air defences. But much of the time it may well be a by-product of poor automation design coupled with companies hustling aggressively for consumers’ attention.

This suggests a missing factor in machine-learning research: manners. We’ve evolved manners to signal to others that our intent is not hostile, and to negotiate the many little transactions that in a hostile environment might lead to a tussle for dominance. Yet these are hard for robots. Food-delivery robots can become unpopular for obstructing and harassing other pavement users; and one of the show-stoppers for automated driving is the difficulty that self-driving cars have in crossing traffic, or otherwise negotiating precedence with other road users. And even in the military, manners have a role – from the chivalry codes of medieval knights to the more modern protocols whereby warships and warplanes warn other craft before opening fire. If we let loose swarms of killer drones with no manners, conflict will be more likely.

Our paper Situational Awareness and Machine Learning – Robots, Manners and Stress was invited as a keynote for two co-located events: IEEE CogSIMA and the NATO STO SCI-341 Research Symposium on Situation awareness of Swarms and Autonomous systems. We got so many conflicting demands from the IEEE that we gave up on making a video of the talk for them, and our paper was pulled from their proceedings. However we decided to put the paper online for the benefit of the NATO folks, who were blameless in this matter.

Infrastructure – the Good, the Bad and the Ugly

Infrastructure used to be regulated and boring; the phones just worked and water just came out of the tap. Software has changed all that, and the systems our society relies on are ever more complex and contested. We have seen Twitter silencing the US president, Amazon switching off Parler and the police closing down mobile phone networks used by crooks. The EU wants to force chat apps to include porn filters, India wants them to tell the government who messaged whom and when, and the US Department of Justice has launched antitrust cases against Google and Facebook.

Infrastructure – the Good, the Bad and the Ugly analyses the security economics of platforms and services. The existence of platforms such as the Internet and cloud services enabled startups like YouTube and Instagram soar to huge valuations almost overnight, with only a handful of staff. But criminals also build infrastructure, from botnets through malware-as-a-service. There’s also dual-use infrastructure, from Tor to bitcoins, with entangled legitimate and criminal applications. So crime can scale too. And even “respectable” infrastructure has disruptive uses. Social media enabled both Barack Obama and Donald Trump to outflank the political establishment and win power; they have also been used to foment communal violence in Asia. How are we to make sense of all this?

I argue that this is not simply a matter for antitrust lawyers, but that computer scientists also have some insights to offer, and the interaction between technical and social factors is critical. I suggest a number of principles to guide analysis. First, what actors or technical systems have the power to exclude? Such control points tend to be at least partially social, as social structures like networks of friends and followers have more inertia. Even where control points exist, enforcement often fails because defenders are organised in the wrong institutions, or otherwise fail to have the right incentives; many defenders, from payment systems to abuse teams, focus on process rather than outcomes.

There are implications for policy. The agencies often ask for back doors into systems, but these help intelligence more than interdiction. To really push back on crime and abuse, we will need institutional reform of regulators and other defenders. We may also want to complement our current law-enforcement strategy of decapitation – taking down key pieces of criminal infrastructure such as botnets and underground markets – with pressure on maintainability. It may make a real difference if we can push up offenders’ transaction costs, as online criminal enterprises rely more on agility than on on long-lived, critical, redundant platforms.

This was a Dertouzos Distinguished Lecture at MIT in March 2021.

WEIS 2020 – Liveblog

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.

Our new “Freedom of Speech” policy

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?

Three Paper Thursday: Broken Hearts and Empty Wallets

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.

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Of testing centres, snipe, and wild geese: COVID briefing paper #8

Does the road wind up-hill all the way?
   Yes, to the very end.
Will the day's journey take the whole long day?
   From morn to night, my friend.

Christina Rossetti, 1861: Up-Hill. 

This week’s COVID briefing paper takes a personal perspective as I recount my many adventures in complying with a call for testing from my local council.

So as to immerse the reader in the experience, this post is long. If you don’t have time for that, you can go directly to the briefing.

The council calls for everyone in my street to be tested

On Thursday 13 August my household received a hand-delivered letter from the chief executive of my local council. There had been an increase in cases in my area, and as a result, they were asking everyone on my street to get tested.

Dramatis personae:

  • ME, a knowledge worker who has structured her life so as to minimize interaction with the outside world until the number of daily cases drops a lot lower than it is now;
  • OTHER HOUSEHOLD MEMBERS, including people with health conditions, who would be shielding if shielding hadn’t ended on August 1.

Fortunately, everyone else in my household is also in a position to enjoy the mixed blessing of a lifestyle without social interaction. So, none of us reacted to the news of an outbreak amongst our neighbours with fear for our own health, considering our habits over the last six months. Rather, we were, and are, reassured that the local government was taking a lead.

My neighbour, however, was having a different experience. Like most people on our street, he does not have the same privileges I do: he works in a supermarket, he does not have a car, and his only Internet access is through his dumbphone. Days before, he had texted me at the end of his tether, because customers were not wearing masks or observing social distancing. He felt (because he is) unprotected, and said it was only a matter of time before he becomes infected. Receiving the council’s letter only reinforced his alarm.

Booking the tests

Continue reading Of testing centres, snipe, and wild geese: COVID briefing paper #8

Three paper Thursday: Ethics in security research

Good security and cybercrime research often creates an impact and we want to ensure that impact is positive. This week I will discuss three papers on ethics in computer security research in the run up to next week’s Security and Human Behaviour workshop (SHB). Ethical issues in research using datasets of illicit origin (Thomas, Pastrana, Hutchings, Clayton, Beresford) from IMC 2017, Measuring eWhoring (Pastrana, Hutchings, Thomas, Tapiador) from IMC 2019, and An Ethics Framework for Research into Heterogeneous Systems (Happa, Nurse, Goldsmith, Creese, Williams).

Ethical issues in research using datasets of illicit origin (blog post) came about because in prior work we had noticed that there were ethical complexities to take care of when using data that had “fallen off the back of a lorry” such as the backend databases of hacked booter services that we had used. We took a broad look at existing published guidance to synthesise those issues which particularly apply to using data of illicit origin and we expected to see discussed by researchers:

Continue reading Three paper Thursday: Ethics in security research