I’m at the 22nd Workshop on the Economics of Information Security in Geneva, and will be liveblogging the sessions in the followups to this post. Links to previous editions of WEIS, along with liveblogs and recordings, may be found here.
Category Archives: Security psychology
Security and Human Behaviour 2023
The Workshop on Security and Human Behaviour is happening right now in Carnegie-Mellon University and I’ll be liveblogging it in followups to this post. The participants’ papers are here, while the liveblogs and papers from previous workshops are here.
Interop: One Protocol to Rule Them All?
Everyone’s worried that the UK Online Safety Bill and the EU Child Sex Abuse Regulation will put an end to end-to-end encryption. But might a law already passed by the EU have the same effect?
The Digital Markets Act ruled that users on different platforms should be able to exchange messages with each other. This opens up a real Pandora’s box. How will the networks manage keys, authenticate users, and moderate content? How much metadata will have to be shared, and how?
In our latest paper, One Protocol to Rule Them All? On Securing Interoperable Messaging, we explore the security tensions, the conflicts of interest, the usability traps, and the likely consequences for individual and institutional behaviour.
Interoperability will vastly increase the attack surface at every level in the stack – from the cryptography up through usability to commercial incentives and the opportunities for government interference.
Twenty-five years ago, we warned that key escrow mechanisms would endanger cryptography by increasing complexity, even if the escrow keys themselves can be kept perfectly secure. Interoperability is complexity on steroids.
Bugs still considered harmful
A number of governments are trying to mandate surveillance software in devices that support end-to-end encrypted chat; the EU’s CSA Regulation and the UK’s Online Safety bill being two prominent current examples. Colleagues and I wrote Bugs in Our Pockets in 2021 to point out what was likely to go wrong; GCHQ responded with arguments about child protection, which I countered in my paper Chat Control or Child Protection.
As lawmakers continue to discuss the policy, the latest round in the technical argument comes from the Rephrain project, which was tasked with evaluating five prototypes built with money from GCHQ and the Home Office. Their report may be worth a read.
One contender looks for known-bad photos and videos with software on both client and server, and is the only team with access to CSAM for training or testing (it has the IWF as a partner). However it has inadequate controls both against scope creep, and against false positives and malicious accusations.
Another is an E2EE communications tool with added profanity filter and image scanning, linked to age verification, with no safeguards except human moderation at the reporting server.
The other three contenders are nudity detectors with various combinations of age verification or detection, and of reporting to parents or service providers.
None of these prototypes comes close to meeting reasonable requirements for efficacy and privacy. So the project can be seen as empirical support for the argument we made in “Bugs”, namely that doing surveillance while respecting privacy is really hard.
Security economics course
Back in 2015 I helped record a course in security economics in a project driven by colleagues from Delft. This was launched as an EDX MOOC as well as becoming part of the Delft syllabus, and it has been used in many other courses worldwide. In Brussels, in December, a Ukrainian officer told me they use it in their cyber defence boot camp.
There’s been a lot of progress in security economics over the past seven years; see for example the liveblogs of the workshop on the economics of information security here. So it’s time to update the course, and we’ll be working on that between now and May.
If there are any topics you think we should cover, or any bugs you’d like to report, please get in touch!
WEIS 2022 – Liveblog
I’m at the twenty-first Workshop on the Economics of Information Security, hosted by the University of Tulsa. I’ll be liveblogging the sessions in followups to this post. There’s a live stream for which you can register here.
Security and Human Behaviour 2022
The Workshop on Security and Human Behaviour is now an in-person event once more, after two years online. It’s happening in Cambridge and I’ll be liveblogging it in followups to this post. Edited to add: Here are the videos for sessions 1, 2, 3, 4, 5, 6, 7 and 8.
WEIS 2022 call for papers
The 2022 Workshop on the Economics of Information Security will be held at Tulsa, Oklahoma, on 21-22 June 2022. Paper submissions are due by 28 February 2022. After two virtual events we’re eager to get back to meeting in person if we possibly can.
The program chairs for 2022 are Sadia Afroz and Laura Brandimarte, and here is the call for papers.
We originally set this as 20-21, being unaware that June 20 is the Juneteenth holiday in the USA. Sorry about that.
Anyway, we hope to see lots of you in Tulsa!
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.