Analyzing privacy aspects of the W3C Vibration API

When making web standards, multiple scenarios possibly affecting privacy are considered. This includes even extreme ones; and this is a good thing. It’s best to predict the creative use and abuse of web features, before they are exploited.

Vibration API

The mechanism allowing websites to utilize a device’s vibration motor is called the Vibration API. The mechanism allows a device to be vibrated in particular patterns. The argument to the vibration() function is a list called a pattern. The list’s odd indices cause a vibration for a specific length of time, and even values are the still periods. For example, a web designer can make the device to vibrate for a specific duration, say 50 ms and follow that with a still period of 100 ms using the following call:

navigator.vibration([50,100])

In certain circumstances this can create several interesting potential privacy risks. Let’s look at the Vibration API from a privacy point of view. I will consider a number of scenarios on various technical levels.

Toy de-anonymisation scenario

One potential risk is the identification of a particular person in real life. Imagine several people in the same room placing their devices on a table. At some point, one person’s device vibrates in specific patterns. This individual might then become marked to a potential observer.

How could such a script be delivered? One possibility is though web advertising infrastructures. These offer capabilities of targeting individuals with a considerable accuracy (with respect to their location).

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Microsoft Ireland: winning the battle for privacy but losing the war

On Thursday, Microsoft won an important federal appeals court case against the US government. The case centres on a warrant issued in December 2013, requiring Microsoft to disclose emails and other records for a particular msn.com email address which was related to a narcotics investigation. It transpired that these emails were stored in a Microsoft datacenter in Ireland, but the US government argued that, since Microsoft is a US company and can easily copy the data into the US, a US warrant would suffice. Microsoft argued that the proper way for the US government to obtain the data is through the Mutual Legal Assistance Treaty (MLAT) between the US and Ireland, where an Irish court would decide, according to Irish law, whether the data should be handed over to US authorities. Part of the US government’s objection to this approach was that the MLAT process is sometimes very slow, although though the Irish government has committed to consider any such request “expeditiously”.

The appeal court decision is an important victory for Microsoft (following two lower courts ruling against them) because they sell their european datacenters as giving their european customers confidence that their data will be subject to the more stringent european privacy laws. Microsoft’s case was understandably supported by other technology companies in the same position, as well as civil liberties organisations such as the Electronic Frontier Foundation in the US and the Open Rights Group in the UK. However, I have mixed opinions about the outcome: while probably the right decision in this case, the wider consequences could be detrimental to privacy.

Both sides of the case wanted to set a precedent (if not legally, at least in practice). The US government wanted US law to apply to data held by US companies, wherever in the world the data resides. Microsoft wanted the location of the data to imply which legal regime applied, and so their customers could be confident that their own country’s laws will be respected, provided Microsoft have a datacenter in their own country (or at least one with compatible laws). My concern is that this ruling will give false assurance to customers of US companies, because in other circumstances a different decision could quite easily be taken.

We know about this case because Microsoft chose to challenge it in court, and were able to do so. This is the first time Microsoft has challenged a US warrant for data stored in their Irish datacenter despite it being in operation for three years prior to the case. Had the email address been associated with a more serious crime, or the demand for emails accompanied by a gagging order, it may not have been challenged. Microsoft and other technology companies may still choose to accept, or may even be forced to accept, the applicability of future US warrants to data they control, regardless of the court decision last week. One extreme approach to compel this approach would be for the US to jail employees until their demands are complied with.

For this reason, I have argued that control over data is more important than where data resides. If a company does not have the technical capability to comply with an order, it is easier for them to defend their case, and so protects both the company’s customers and staff. Microsoft have taken precisely this approach for their new German datacenters, which will be operated by staff in Germany working for a German “data trustee” (Deutsche Telekom). In contrast to their Irish datacenter, Microsoft staff will be unable to access customer data, except with the permission of and oversight from the data trustee.

While the data trustee model resists information being obtained through improper legal means, a malicious employee could still break rules for personal gain, or the systems designed to process legal requests could be hacked into. With modern security techniques it is possible to do better. End-to-end encryption for instant messaging is one such example, because (if designed properly) the communications provider does not have access to messages they carry. A more sophisticated approach is “distributed consensus”, where a decision is only taken if a majority of participants agree. The consensus process is automated and enforced through cryptography, ensuring that rules are respected even if some participants are malicious. Critical decisions in the Tor network and in Bitcoin are taken this way. More generally, there is a growing recognition that purely legal or procedural mechanisms are insufficient to protect privacy. This is one of the common threads present in much of the research presented at the Privacy Enhancing Technologies Symposium, being held this week in Darmstadt: recognising that there will always be imperfections in software, people and procedures and showing that nevertheless individual’s privacy can still be protected.

Exceptional access provisions in the Investigatory Powers Bill

The Investigatory Powers Bill, being debated in Parliament this week, proposes the first wide-scale update in 15 years to the surveillance powers of the UK law-enforcement and intelligence agencies.

The Bill has several goals: to consolidate some existing surveillance powers currently either scattered throughout other legislation or not even publicly disclosed, to create a wide range of new surveillance powers, and to change the process of authorisation and oversight surrounding the use of surveillance powers. The Bill is complex and, at 245 pages long, makes scrutiny challenging.

The Bill has had its first and second readings in the House of Commons, and has been examined by relevant committees in the Commons. The Bill will now be debated in the ‘report stage’, where MPs will have the chance to propose amendments following committee scrutiny. After this it will progress to a third reading, and then to the House of Lords for further debate, followed by final agreement by both Houses.

So far, four committee reports have been published examining the draft Bill, from the Intelligence and Security Committee of Parliament, the joint House of Lords/House of Commons committee specifically set up to examine the draft Bill, the House of Commons Science and Technology committee (to which I served as technical advisor) and the Joint Committee on Human Rights.

These committees were faced with a difficult task of meeting an accelerated timetable for the Bill, with the government aiming to have it become law by the end of 2016. The reason for the haste is that the Bill would re-instate and extend the ability of the government to compel companies to collect data about their users, even without there being any suspicion of wrongdoing, known as “data retention”. This power was previously set out in the EU Data Retention Directive, but in 2014 the European Court of Justice found it be unlawful.

Emergency legislation passed to temporarily permit the government to continue their activities will expire in December 2016 (but may be repealed earlier if an appeal to the European Court of Justice succeeds).

The four committees which examined the Bill together made 130 recommendations but since the draft was published, the government only slightly changed the Bill, and only a few minor amendments were accepted by the Public Bills committee.

Many questions remain about whether the powers granted by the Bill are justifiable and subject to adequate oversight, but where insights from computer security research are particularly relevant is on the powers to grant law enforcement the ability to bypass normal security mechanisms, sometimes termed “exceptional access”.

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Come work with us!

I’m very pleased to announce that — along with George Danezis and Tomaso Aste, head of our Financial Computing group — I’ve been awarded a grant to continue our work on distributed ledgers (aka “blockchain-like things”) for the next three years.

Our group has already done a lot of research in this space, including George’s and my recent paper on centrally banked cryptocurrencies (at NDSS 2016) and Jens’ paper (along with Markulf Kohlweiss, a frequent UCL collaborator) on efficient ring signatures and applications to Zerocoin-style cryptocurrencies (at Eurocrypt 2015).  It’s great to have this opportunity to further investigate the challenges in this space and develop our vision for the future of these technologies, so big thanks to the EPSRC!

Anyway, the point of this post is to advertise, as part of this grant, three positions for postdoctoral researchers.  We are also seeking collaboration with any industrial partners investigating the potential usage of distributed ledgers, and in particular ones looking at the application of these ledgers across the following settings (or with a whole new setting in mind!):

  • Identity management. How can identities be stored, shared, and issued in a way that preserves privacy, prevents theft and fraud, and allows for informal forms of identity in places where no formal ones exist?
  • Supply chain transparency. How can supply chain information be stored in a way that proves integrity, preserves the privacy of individual actors, and can be presented to the end customer in a productive way?
  • Financial settlement. How can banking information be stored in a way that allows banks to easily perform gross settlement, reduces the burden on a central bank, and enables auditability of the proper functioning of the system?
  • Administration of benefits. How can benefits be administered to and used by disadvantaged populations in a way that preserves privacy, provides useful visibility into their spending, and protects against potential abuses of the system?

We expect the postdoctoral researchers to work with us and with each other on the many exciting problems in this space, which are spread across cryptography, computer and network security, behavioural economics, distributed systems, usable security, human-computer interaction, and software engineering (just to name a few!).  I encourage anyone interested to reach out to me (Sarah) to discuss this further, whether or not they’ve already done research on the particular topic of distributed ledgers.

That’s all for now, but please get in touch with me if you have any questions, and in the years to come I hope to invite many people to come work with us in London and to announce the various outcomes of this exciting project!

Privately gathering statistics and training simple models

Last week, Luca Melis has presented our NDSS16 paper “Efficient Private Statistics with Succinct Sketches“, where we show how to privately and efficiently aggregate data from many sources and/or large streams, and then use the aggregate to extract useful statistics and train simple machine learning models.

Our work is motivated by a few “real-world” problems:

  • Media broadcasting providers like the BBC (with which we collaborate) routinely collect data from their users about videos they have watched (e.g., on BBC’s iPlayer) in order to provide users with personalized suggestions for other videos, based on recommender systems like Item k-Nearest Neighbor (ItemKNN)
  • Urban and transport planning committees, such as London’s mass transport operators, need to gather statistics about people’s movements and commutes, e.g., to improve transportation services and predict near-future trends and anomalies on a short notice.
  • Network infrastructures like the Tor network need to gather traffic statistics, like the number of, and traffic generated by, Tor hidden services, in order to tune design decisions as well as convince their founders the infrastructure is used for the intended purposes.

While different in their application, these examples exhibit a common feature: the need for providers to aggregate large amounts of sensitive information from large numbers of data sources, in order to produce aggregate statistics and possibly train machine learning models.

Prior work has proposed a few cryptographic tools for privacy-enhanced computation that could be use for private collection of statistics. For instance, by relying on homomorphic encryption and/or secret sharing, an untrusted aggregator can receive encrypted readings from users and only decrypt their sum. However, these require users to perform a number of cryptographic operations, and transmit a number of ciphertexts, linear in the size of their inputs, which makes it impractical for the scenarios discussed above, whereby inputs to be aggregated are quite large. For instance, if we use ItemKNN for the recommendations, we would need to aggregate values for “co-views” (i.e., videos that have been watched by the same user) of hundreds of videos at the time – thus, each user would have to encrypt and transfer hundreds of thousands of values at the time.

Scaling private aggregation

We tackle the problem from two points of view: an “algorithmic” one and a “system” one. That is, we have worked both on the design of the necessary cryptographic and data structure tools, as well as on making it easy for application developers to easily support these tools in web and mobile applications.

Our intuition is that, in many scenarios, it might be enough to collect estimates of statistics and trade off an upper-bounded error with significant efficiency gains. For instance, the accuracy of a recommender system might not be really affected if the statistics we need to train the model are approximated with a small error.

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“Do you see what I see?” ask Tor users, as a large number of websites reject them but accept non-Tor users

If you use an anonymity network such as Tor on a regular basis, you are probably familiar with various annoyances in your web browsing experience, ranging from pages saying “Access denied” to having to solve CAPTCHAs before continuing. Interestingly, these hurdles disappear if the same website is accessed without Tor. The growing trend of websites extending this kind of “differential treatment” to anonymous users undermines Tor’s overall utility, and adds a new dimension to the traditional threats to Tor (attacks on user privacy, or governments blocking access to Tor). There is plenty of anecdotal evidence about Tor users experiencing difficulties in browsing the web, for example the user-reported catalog of services blocking Tor. However, we don’t have sufficient detail about the problem to answer deeper questions like: how prevalent is differential treatment of Tor on the web; are there any centralized players with Tor-unfriendly policies that have a magnified effect on the browsing experience of Tor users; can we identify patterns in where these Tor-unfriendly websites are hosted (or located), and so forth.

Today we present our paper on this topic: “Do You See What I See? Differential Treatment of Anonymous Users” at the Network and Distributed System Security Symposium (NDSS). Together with researchers from the University of Cambridge, University College London, University of California, Berkeley and International Computer Science Institute (Berkeley), we conducted comprehensive network measurements to shed light on websites that block Tor. At the network layer, we scanned the entire IPv4 address space on port 80 from Tor exit nodes. At the application layer, we fetch the homepage from the most popular 1,000 websites (according to Alexa) from all Tor exit nodes. We compare these measurements with a baseline from non-Tor control measurements, and uncover significant evidence of Tor blocking. We estimate that at least 1.3 million IP addresses that would otherwise allow a TCP handshake on port 80 block the handshake if it originates from a Tor exit node. We also show that at least 3.67% of the most popular 1,000 websites block Tor users at the application layer.

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Insecure by design: protocols for encrypted phone calls

The MIKEY-SAKKE protocol is being promoted by the UK government as a better way to secure phone calls. The reality is that MIKEY-SAKKE is designed to offer minimal security while allowing undetectable mass surveillance, through the introduction a backdoor based around mandatory key-escrow. This weakness has implications which go further than just the security of phone calls.

The current state of security for phone calls leaves a lot to be desired. Land-line calls are almost entirely unencrypted, and cellphone calls are also unencrypted except for the radio link between the handset and the phone network. While the latest cryptography standards for cellphones (3G and 4G) are reasonably strong it is possible to force a phone to fall back to older standards with easy-to-break cryptography, if any. The vast majority of phones will not reveal to their user whether such an attack is under way.

The only reason that eavesdropping on land-line calls is not commonplace is that getting access to the closed phone networks is not as easy compared to the more open Internet, and cellphone cryptography designers relied on the equipment necessary to intercept the radio link being only affordable by well-funded government intelligence agencies, and not by criminals or for corporate espionage. That might have been true in the past but it certainly no longer the case with the necessary equipment now available for $1,500. Governments, companies and individuals are increasingly looking for better security.

A second driver for better phone call encryption is the convergence of Internet and phone networks. The LTE (Long-Term Evolution) 4G cellphone standard – under development by the 3rd Generation Partnership Project (3GPP) – carries voice calls over IP packets, and desktop phones in companies are increasingly carrying voice over IP (VoIP) too. Because voice calls may travel over the Internet, whatever security was offered by the closed phone networks is gone and so other security mechanisms are needed.

Like Internet data encryption, voice encryption can broadly be categorised as either link encryption, where each intermediary may encrypt data before passing it onto the next, or end-to-end encryption, where communications are encrypted such that only the legitimate end-points can have access to the unencrypted communication. End-to-end encryption is preferable for security because it avoids intermediaries being able to eavesdrop on communications and gives the end-points assurance that communications will indeed be encrypted all the way to their other communication partner.

Current cellphone encryption standards are link encryption: the phone encrypts calls between it and the phone network using cryptographic keys stored on the Subscriber Identity Module (SIM). Within the phone network, encryption may also be present but the network provider still has access to unencrypted data, so even ignoring the vulnerability to fall-back attacks on the radio link, the network providers and their suppliers are weak points that are tempting for attackers to compromise. Recent examples of such attacks include the compromise of the phone networks of Vodafone in Greece (2004) and Belgacom in Belgium (2012), and the SIM card supplier Gemalto in France (2010). The identity of the Vodafone Greece hacker remains unknown (though the NSA is suspected) but the attacks against Belgacom and Gemalto were carried out by the UK signals intelligence agency – GCHQ – and only publicly revealed from the Snowden leaks, so it is quite possible there are others attacks which remain hidden.

Email is typically only secured by link encryption, if at all, with HTTPS encrypting access to most webmail and Transport Layer Security (TLS) sometimes encrypting other communication protocols that carry email (SMTP, IMAP and POP). Again, the fact that intermediaries have access to plaintext creates a vulnerability, as demonstrated by the 2009 hack of Google’s Gmail likely originating from China. End-to-end email encryption is possible using the OpenPGP or S/MIME protocols but their use is not common, primarily due to their poor usability, which in turn is at least partially a result of having to stay compatible with older insecure email standards.

In contrast, instant messaging applications had more opportunity to start with a clean-slate (because there is no expectation of compatibility among different networks) and so this is where much innovation in terms of end-to-end security has taken place. Secure voice communication however has had less attention than instant messaging so in the remainder of the article we shall examine what should be expected of a secure voice communication system, and in particular see how one of the latest and up-coming protocols, MIKEY-SAKKE, which comes with UK government backing, meets these criteria.

MIKEY-SAKKE and Secure Chorus

MIKEY-SAKKE is the security protocol behind the Secure Chorus voice (and also video) encryption standard, commissioned and designed by GCHQ through their information security arm, CESG. GCHQ have announced that they will only certify voice encryption products through their Commercial Product Assurance (CPA) security evaluation scheme if the product implements MIKEY-SAKKE and Secure Chorus. As a result, MIKEY-SAKKE has a monopoly over the vast majority of classified UK government voice communication and so companies developing secure voice communication systems must implement it in order to gain access to this market. GCHQ can also set requirements of what products are used in the public sector and as well as for companies operating critical national infrastructure.

UK government standards are also influential in guiding purchase decisions outside of government and we are already seeing MIKEY-SAKKE marketed commercially as “government-grade security” and capitalising on their approval for use in the UK government. For this reason, and also because GCHQ have provided implementers a free open source library to make it easier and cheaper to deploy Secure Chorus, we can expect wide use MIKEY-SAKKE in industry and possibly among the public. It is therefore important to consider whether MIKEY-SAKKE is appropriate for wide-scale use. For the reasons outlined in the remainder of this article, the answer is no – MIKEY-SAKKE is designed to offer minimal security while allowing undetectable mass surveillance though key-escrow, not to provide effective security.

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ACE-CSR opening event 2015/16: talks on malware, location privacy and wiretap law

The opening event for the UCL Academic Centre of Excellence for Cyber Security Research in the 2015–2016 academic term featured three speakers: Earl Barr, whose work on approximating program equivalence has won several ACM distinguished paper awards; Mirco Musolesi from the Department of Geography, whose background includes a degree in computer science and an interest in analysing myriad types of data while protecting privacy; and Susan Landau, a professor at Worcester Polytechnic Institute and a visiting professor at UCL and an expert on cyber security policy whose books include Privacy On the Line: the Politics of Wiretapping and Encryption (with Whitfield Diffie) and Surveillance or Security? The Risks Posed by New Wiretapping Technologies.

Detecting malware and IP theft through program similarity

Earl Barr is a member of the software systems engineering group and the Centre for Research on Evolution, Search, and Testing. His talk outlined his work using program similarity to determine whether two arbitrary programs have the same behaviour in two areas relevant to cyber security: malware and intellectual property theft in binaries (that is, code reused in violation of its licence).

Barr began by outlining his work on detecting malware, comparing the problem to that facing airport security personnel trying to find a terrorist among millions of passengers. The work begins with profiling: collect two zoos, and then ask if the program under consideration is more likely to belong to the benign zoo or the malware zoo.

Rather than study the structure of the binary, Barr works by viewing the program as strings of 0s and 1s, which may not coincide with the program’s instructions, and using information theory to create a measure of dissimilarity, the normalised compression distance (NCD). The NCD serves as an approximation of the Kolmogorov Complexity, a mathematical measure of the complexity of the shortest description of an object, which is then normalised using a compression algorithm that ignores the details of the instruction set architecture for which the binary is written.

Using these techniques to analyse a malware zoo collected from sources such as Virus Watch, Barr was able to achieve a 95.7% accuracy rate. He believes that although this technique isn’t suitable for contemporary desktop anti-virus software, it opens a new front in the malware detection arms race. Still, Barr is aware that malware writers will rapidly develop countermeasures and his group is already investigating counter-countermeasures.

Malware writers have three avenues for blocking detection: injecting new content that looks benign; encryption; and obfuscation. Adding new content threatens the malware’s viability: raising the NCD by 50% requires doubling the size of the malware. Encryption can be used against the malware writer: applying a language model across the program reveals a distinctive saw-toothed pattern of regions with low surprise and low entropy alternating with regions of high surprise and high entropy (that is, regions with ciphertext). Obfuscation is still under study: the group is using three obfuscation engines available for Java and applying them repeatedly to Java malware. Measuring the NCD after each application shows that after 100 iterations the NCD approaches 1 (that is, the two items being compared are dissimilar), but that two of the three engines make errors after 200 applications. Unfortunately for malware writers, this technique also causes the program to grow in size. The cost of obfuscation to malware writers may therefore be greater than that imposed upon white hats.

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How Tor’s privacy was (momentarily) broken, and the questions it raises

Just how secure is Tor, one of the most widely used internet privacy tools? Court documents released from the Silk Road 2.0 trial suggest that a “university-based research institute” provided information that broke Tor’s privacy protections, helping identify the operator of the illicit online marketplace.

Silk Road and its successor Silk Road 2.0 were run as a Tor hidden service, an anonymised website accessible only over the Tor network which protects the identity of those running the site and those using it. The same technology is used to protect the privacy of visitors to other websites including journalists reporting on mafia activity, search engines and social networks, so the security of Tor is of critical importance to many.

How Tor’s privacy shield works

Almost 97% of Tor traffic is from those using Tor to anonymise their use of standard websites outside the network. To do so a path is created through the Tor network via three computers (nodes) selected at random: a first node entering the network, a middle node (or nodes), and a final node from which the communication exits the Tor network and passes to the destination website. The first node knows the user’s address, the last node knows the site being accessed, but no node knows both.

The remaining 3% of Tor traffic is to hidden services. These websites use “.onion” addresses stored in a hidden service directory. The user first requests information on how to contact the hidden service website, then both the user and the website make the three-hop path through the Tor network to a rendezvous point which joins the two connections and allows both parties to communicate.

In both cases, if a malicious operator simultaneously controls both the first and last nodes to the Tor network then it is possible to link the incoming and outgoing traffic and potentially identify the user. To prevent this, the Tor network is designed from the outset to have sufficient diversity in terms of who runs nodes and where they are located – and the way that nodes are selected will avoid choosing closely related nodes, so as to reduce the likelihood of a user’s privacy being compromised.

How Tor works
How Tor works (source: EFF)

This type of design is known as distributed trust: compromising any single computer should not be enough to break the security the system offers (although compromising a large proportion of the network is still a problem). Distributed trust systems protect not only the users, but also the operators; because the operators cannot break the users’ anonymity – they do not have the “keys” themselves – they are less likely to be targeted by attackers.

Unpeeling the onion skin

With about 2m daily users Tor is by far the most widely used privacy system and is considered one of the most secure, so research that demonstrates the existence of a vulnerability is important. Most research examines how to increase the likelihood of an attacker controlling both the first and last node in a connection, or how to link incoming traffic to outgoing.

When the 2014 programme for the annual BlackHat conference was announced, it included a talk by a team of researchers from CERT, a Carnegie Mellon University research institute, claiming to have found a means to compromise Tor. But the talk was cancelled and, unusually, the researchers did not give advance notice of the vulnerability to the Tor Project in order for them to examine and fix it where necessary.

This decision was particularly strange given that CERT is worldwide coordinator for ensuring software vendors are notified of vulnerabilities in their products so they can fix them before criminals can exploit them. However, the CERT researchers gave enough hints that Tor developers were able to investigate what had happened. When they examined the network they found someone was indeed attacking Tor users using a technique that matched CERT’s description.

The multiple node attack

The attack turned on a means to tamper with a user’s traffic as they looked up the .onion address in the hidden service directory, or in the hidden service’s traffic as it uploaded the information to the directory.

Continue reading How Tor’s privacy was (momentarily) broken, and the questions it raises

New EU Innovative Training Network project “Privacy & Us”

Last week, “Privacy & Us” — an Innovative Training Network (ITN) project funded by the EU’s Marie Skłodowska-Curie actions — held its kick-off meeting in Munich. Hosted in the nice and modern Wisschenschafts Zentrum campus by Uniscon, one of the project partners, principal investigators from seven different countries set out the plan for the next 48 months.

Privacy & Us really stands for “Privacy and Usability” and aims to conduct privacy research and, over the next 3 years, train thirteen Early Stage Researchers (ESRs) — i.e., PhD students — to be able to reason, design, and develop innovative solutions to privacy research challenges, not only from a technical point of view but also from the “human side”.

The project involves nine “beneficiaries”: Karlstads Universitet (Sweden), Goethe Universitaet Frankfurt (Germany), Tel Aviv University (Israel), Unabhängiges Landeszentrum für Datenschutz (Germany), Uniscon (Germany), University College London (UK), USECON (Austria), VASCO Innovation Center (UK), and Wirtschaft Universitat Wien (Austria), as well as seven partner organizations: the Austrian Data Protection Authority (Austria), Preslmayr Rechtsanwälte OG (Austria), Friedrich-Alexander University Erlangen (Germany), University of Bonn (Germany), the Bavarian Data Protection Authority (Germany), EveryWare Technologies (Italy), and Sentor MSS AB (Sweden).

The people behind Privacy & Us project at the kick-off meeting in Munich, December 2015
The people behind Privacy & Us project at the kick-off meeting in Munich, December 2015

The Innovative Training Networks are interdisciplinary and multidisciplinary in nature and promote, by design, a collaborative approach to research training. Funding is extremely competitive, with acceptance rate as low as 6%, and quite generous for the ESRs who often enjoy higher than usual salaries (exact numbers depend on the hosting country), plus 600 EUR/month mobility allowance and 500 EUR/month family allowance.

The students will start in August 2016 and will be trained to face both current and future challenges in the area of privacy and usability, spending a minimum of six months in secondment to another partner organization, and participating in several training and development activities.

Three studentships will be hosted at UCL,  under the supervision of Dr Emiliano De Cristofaro, Prof. Angela Sasse, Prof. Ann Blandford, and Dr Steven Murdoch. Specifically, one project will investigate how to securely and efficiently store genomic data, design and implementing privacy-preserving genomic testing, as well as support user-centered design of secure personal genomic applications. The second project will aim to better understand and support individuals’ decision-making around healthcare data disclosure, weighing up personal and societal costs and benefits of disclosure, and the third (with the VASCO Innovation Centre) will explore techniques for privacy-preserving authentication, namely, extending these to develop and evaluate innovative solutions for secure and usable authentication that respects user privacy.

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