Category Archives: Privacy technology

Anonymous communication, data protection

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.

Hiring for AP4L

I’m hiring a Research Assistant/Associate to work on the EPSRC-funded Adaptive PETs to Protect & emPower People during Life Transitions (AP4L) project. The project is being undertaken with the Universities of Surrey, Queen Mary, Strathclyde, Edge Hill, and Edinburgh.

AP4L is a program of interdisciplinary research, centring on the online privacy & vulnerability challenges that people face when going through major life transitions. The four transitions we are considering in the scope of this project are relationship breakdowns; LBGT+ transitions or transitioning gender; entering/ leaving employment in the Armed Forces; and developing a serious illness or becoming terminally ill. Our central goal is to develop privacy-by-design technologies to protect & empower people during these transitions.

We are looking for a researcher with experience in quantitative data analysis, threat assessment, data science, machine learning and/or natural language processing, as well as excellent programming and technical writing skills. Expertise in cybercrime or privacy enhancing technologies (PETs) research is desirable, but not essential. Successful applicants will review the relevant literature, design research projects, develop tools, collect and analyse data, and write research outputs.

The role will analyse life transitions from the attacker’s perspective, such as how and where they gather information about their victims. This will require the analysis of cybercrime forums and similar data at scale. Furthermore, the tools we develop are designed for an adversarial context. Adversaries include those known to individuals, such as interfamilial abuse, as well as targeted and indiscriminate attacks. The researcher will also undertake a rigorous threat analysis for each of the tools developed within the overall project.

The full details are available here.

Chatcontrol or Child Protection?

Today I publish a detailed rebuttal to the argument from the intelligence community that we need to break end-to-end encryption in order to protect children. This has led in the UK to the Online Safety Bill and in the EU to the proposed Child Sex Abuse Regulation, which has become known in Brussels as “chatcontrol”.

The intelligence community wants to break WhatsApp, as that carries everything from diplomatic and business negotiations to MPs’ wheeling and dealing. Both the UK and EU proposals will take powers to mandate scanning of both text and images in your phone before messages are encrypted and sent, or after they are received and decrypted.

This is justified with arguments around child protection, which require careful study. Most child abuse happens in dysfunctional families, with the abuser typically being the mother’s partner; technology is often abused as a means of extortion and control. Indecent images get shared with outsiders, and user reports of such images are a really important way of alerting the police to new cases. There are also abusers who look for vulnerable minors online, and here too it’s user reporting that does most of the work.

But it costs money to get moderators to respond to user reports of abuse, so the tech firms’ performance here is unimpressive. Facebook seems to be the best of a bad lot, while Twitter is awful – and so hosts a lot more abuse. There’s a strong case for laws to compel service providers to manage user reporting better, and the EU’s Digital Services Act goes some way in this direction. The Online Safety Bill should be amended to do the same, and we produced a policy paper on this last week.

But details matter, as it’s important to understand the many inappropriate laws, dysfunctional institutions and perverse incentives that get in the way of rational policies around the online aspects of crimes of sexual violence against minors. (The same holds for violent online political extremism, which is also used as an excuse for more censorship and surveillance.) We do indeed need to spend more money on reducing violent crime, but it should be spent locally on hiring more police officers and social workers to deal with family violence directly. We also need welfare reform to reduce the number of families living in poverty.

As for surveillance, it has not helped in the past and there is no real prospect that the measures now proposed would help in the future. I go through the relevant evidence in my paper and conclude that “chatcontrol” will not improve child protection, but damage it instead. It will also undermine human rights at a time when we need to face down authoritarians not just technologically and militarily, but morally as well. What’s the point of this struggle, if not to defend democracy, the rule of law, and human rights?

Edited to add: here is a video of a talk I gave on the paper at Digitalize.

CoverDrop: Securing Initial Contact for Whistleblowers

Whistleblowing is dangerous business. Whistleblowers face grave consequences if they’re caught and, to make matters worse, the anonymity set – the set of potential whistleblowers for a given story – is often quite small. Mass surveillance regimes around the world don’t help matters either. Yet whistleblowing has been crucial in exposing corruption, rape and other crimes in recent years. In our latest research paper, CoverDrop: Blowing the Whistle Through A News App, we set out to create a system that allows whistleblowers to securely make initial contact with news organisations. Our paper has been accepted at PETS, the Privacy Enhancing Technologies Symposium.

To work out how we could help whistleblowers release sensitive information to journalists without exposing their identity, we conducted two workshops with journalists, system administrators and software engineers at leading UK-based news organisations. These discussions made it clear that a significant weak point in the whistleblowing chain is the initial contact by the source to the journalist or news organisation. Sources would often get in touch over insecure channels (e.g., email, phone or SMS) and then switch to more secure channels (e.g., Tor and Signal) later on in the conversation – but by then it may be too late. 

Existing whistleblowing solutions such as SecureDrop rely on Tor for anonymity and expect a high degree of technical competence from its users. But in many cases, simply connecting to the Tor network is enough to single out the whistleblower from a small anonymity set. 

CoverDrop takes a different approach. Instead of connecting to Tor, we embed the whistleblowing mechanism in the mobile news app published by respective news organisations and use the traffic generated by all users of the app as cover traffic, hiding any messages from whistleblowers who use it. We implemented CoverDrop and have shown it to be secure against a global passive network adversary that also has the ability to issue warrants on all infrastructure as well as the source and recipient devices.

We instantiated CoverDrop in the form of an Android app with the expectation that news organisations embed CoverDrop in their standard news apps. Embedding CoverDrop into a news app provides the whistleblower with deniability as well as providing a secure means of contact to all users. This should nudge potential whistleblowers away from using insecure methods of initial contact. The whistleblowing component is a modified version of Signal, augmented with dummy messages to prevent traffic analysis. We use the Secure Element on mobile devices, SGX on servers and onion encryption to reduce the ability of an attacker to gain useful knowledge even if some system components are compromised.

The primary limitation of CoverDrop is its messaging bandwidth, which must be kept low to minimise the networking cost borne by the vast majority of news app users who are not whistleblowers. CoverDrop is designed to do a critical and difficult part of whistleblowing: establishing initial contact securely. Once a low-bandwidth communication channel is established, the source and the journalist can meet in person, or use other systems to send large documents.

The full paper can be found here.

Mansoor Ahmed-Rengers, Diana A. Vasile, Daniel Hugenroth, Alastair R. Beresford, and Ross Anderson. CoverDrop: Blowing the Whistle Through A News App. Proceedings on Privacy Enhancing Technologies, 2022.

Security engineering course

This week sees the start of a course on security engineering that Sam Ainsworth and I are teaching. It’s based on the third edition of my Security Engineering book, and is a first cut at a ‘film of the book’.

Each week we will put two lectures online, and here are the first two. Lecture 1 discusses our adversaries, from nation states through cyber-crooks to personal abuse, and the vulnerability life cycle that underlies the ecosystem of attacks. Lecture 2 abstracts this empirical experience into more formal threat models and security policies.

Although our course is designed for masters students and fourth-year undergrads in Edinburgh, we’re making the lectures available to everyone. I’ll link the rest of the videos in followups here, and eventually on the book’s web page.

Rollercoaster: Communicating Efficiently and Anonymously in Large Groups

End-to-end (E2E) encryption is now widely deployed in messaging apps such as WhatsApp and Signal and billions of people around the world have the contents of their message protected against strong adversaries. However, while the message contents are encrypted, their metadata still leaks sensitive information. For example, it is easy for an infrastructure provider to tell which customers are communicating, with whom and when.

Anonymous communication hides this metadata. This is crucial for the protection of individuals such as whistleblowers who expose criminal wrongdoing, activists organising a protest, or embassies coordinating a response to a diplomatic incident. All these face powerful adversaries for whom the communication metadata alone (without knowing the specific message text) can result in harm for the individuals concerned.

Tor is a popular tool that achieves anonymous communication by forwarding messages through multiple intermediate nodes or relays. At each relay the outermost layer of the message is decrypted and the inner message is forwarded to the next relay. An adversary who wants to figure out where A’s messages are finally delivered can attempt to follow a message as it passes through each relay. Alternatively, an adversary might confirm a suspicion that user A talks to user B by observing traffic patterns at A’s and B’s access points to the network instead. If indeed A and B are talking to each other, there will be a correlation between their traffic patterns. For instance, if an adversary observes that A sends three messages and three messages arrive at B shortly afterwards, this provides some evidence that A talks to B. The adversary can increase their certainty by collecting traffic over a longer period of time.

Mix networks such as Loopix use a different design, which defends against such traffic analysis attacks by using (i) traffic shaping and (ii) more intermediate nodes, so called mix nodes. In a simple mix network, each client only sends packets of a fixed length and at predefined intervals (e.g. 1 KiB every 5 seconds). When there is no payload to send, a cover packet is crafted that is indistinguishable to the adversary from a payload packet. If there is more than one payload packet to be sent, packets are queued and sent one by one on the predefined schedule. This traffic shaping ensures that an observer cannot gain any information from observing outgoing network packets. Moreover, mix nodes typically delay each incoming message by a random amount of time before forwarding it (with the delay chosen independently for each message), making it harder for an adversary to correlate a mix node’s incoming and outgoing messages, since they are likely to be reordered. In contrast, Tor relays forward messages as soon as possible in order to minimise latency.

Mix Networks work well for pairwise communication, but we found that group communication creates a unique challenge. Such group communication encompasses both traditional chat groups (e.g. WhatsApp groups or IRC) and collaborative editing (e.g. Google Docs, calendar sync, todo lists) where updates need to be disseminated to all other participants who are viewing or editing the content. There are many scenarios where anonymity requirements meet group communication, such as coordination between activists, diplomatic correspondence between embassies, and organisation of political campaigns.

The traffic shaping of mix networks makes efficient group communication difficult. The limited rate of outgoing messages means that sequentially sending a message to each group member can take a long time. For instance, assuming that the outgoing rate is 1 message every 5 seconds, it will take more than 8 minutes to send the message to all members in a group of size 100. During this process the sender’s output queue is blocked and they cannot send any other messages.

In our paper we propose a scheme named Rollercoaster that greatly improves the latency for group communication in mix networks. The basic idea is that group members who have already received a message can help distribute it to other members of the group. Like a chain reaction, the distribution of the message gains momentum as the number of recipients grows. In an ideal execution of this scheme, the number of users who have received a message doubles with every round, leading to substantially more efficient message delivery across the group.

Rollercoaster works well because there is typically plenty of spare capacity in the network. At any given time most clients will not be actively communicating and they are therefore mostly sending cover traffic. As a result, Rollercoaster actually improves the efficiency of the network and reduces the rate of cover traffic, which in turn reduces the overall required network bandwidth. At the same time, Rollercoaster does not require any changes to the existing Mix network protocol and can benefit from the existing user base and anonymity set.

The basic idea requires more careful consideration in a realistic environment where clients are offline or do not behave faithfully. A fault-tolerant version of our Rollercoaster scheme addresses these concerns by waiting for acknowledgement messages from recipients. If those acknowledgement messages are not received by the sender in a fixed period of time, forwarding roles are reassigned and another delivery attempt is made via a new route. We also show how a single number can seed the generation of a deterministic forwarding schedule. This allows efficient communication of different forwarding schedules and balances individual workloads within the group.

We presented our paper at USENIX Security ‘21 (paper, slides, and recording). It contains more extensions and optimisations than we can summarise here. There is also an extended version available as a tech report with more detailed security arguments in the appendices. The paper reference is:
Daniel Hugenroth, Martin Kleppmann, and Alastair R. Beresford. Rollercoaster: An Efficient Group-Multicast Scheme for Mix Networks. Proceedings of the 30th USENIX Security Symposium (USENIX Security), 2021.

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.