Bitcoin workshop at Financial Crypto 2016

On 26 February 2016 the 3rd workshop on Bitcoin and Blockchain Research in association with Financial Cryptography 2016 took place in Barbados. This workshop aims to bring together researchers interested in cryptocurrencies to present their latest work and discuss together the future of Bitcoin. The program chairs were Sarah Meiklejohn from University College London and Jeremy Clark from Concordia University. The themes addressed during the workshop included blockchain architecture, anonymity, and proof of work alternatives. This event was also a great way for researchers with similar interests to network and share their ideas.

The workshop consisted of 2 keynotes and 4 plenary sessions: Bitcoin network analysis, Enhancing Bitcoin, Ethereum, and Blockchain Architecture.

Nathaniel Popper kicked off the day with a keynote presentation. Nathaniel is a journalist from the New York Times and author of the book ‘Digital Gold: The Untold story of Bitcoin’. He went on to speak about the history of Bitcoin covering Silk Road, Mt Gox, as well as the role of governments.

Then the first session, about Bitcoin network analysis, included two talks. The first one, Stressing Out: Bitcoin Stress Testing, by Khaled Baqer et al., was about DoS attack on Bitcoin, and was presented by Ross Anderson due to visa issues. The second one was Why buy when you can rent? Bribery attacks on Bitcoin-style consensus, by Joseph Bonneau on bribery attacks and cloud mining.

The next session, Enhancing Bitcoin, started with a talk by Ethan Heilman, Blindly Signed Contracts: Anonymous On-Blockchain and Off-Blockchain Bitcoin Transactions, on how to enhance Bitcoin anonymity. Then Mathieu Turuani gave a talk on Automated Verification of Electrum wallet, followed by Aggelos Kiayias on Proof of Proof of Work. Today many light-weight clients use SPV verification instead of full verification. Is it possible to have an even lighter verification? They introduce a modification of the Bitcoin blockchain protocol with sublinear complexity in the length of the chain.

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Biometrics for payments

HSBC and First Direct recently announced that they are introducing fingerprint and voice recognition authentication for customers of online and telephone banking. In my own research, I first found nearly 20 years ago that people who have a multitude of passwords and PINs cannot manage them as security experts want them to. As the number of digital devices and services we use has increased rapidly, managing dozens of login details has become a headache for most people. We recently reported that most bank customers juggle multiple PINs, and are unable to follow the rules that banks set in their contracts. Our research also found that many people dislike the 2-factor token solutions that are currently used by many UK banks.

Passwords as most people use them today are not particularly secure. Attackers can easily attempt to collect information on individuals, using leaks of password files not properly protected by some websites, “phishing” scams or malware planted on people’s computers. Reusing a banking password on other websites – something that many of us do because we cannot remember dozens of different passwords – is also a significant security risk.

The introduction of fingerprint recognition on smartphones – such as the iPhone – has delighted many users fed up with entering their PINs dozens of times a day. So the announcement that HSBC and other banks will be able to use the fingerprint sensor on their smartphones for banking means that millions of consumers will finally be able to end their battle with passwords and PINs and use biometrics instead. Other services people access from their smartphones are likely to follow suit. And given the negative impact that cumbersome authentication via passwords and PINs has on staff productivity and morale in many organisations, we can expect to see biometrics deployed in work contexts, too.

But while biometrics – unlike passwords – do not require mental gymnastics from users, there are different usability challenges. Leveraging the biometric from the modality of interaction – e.g. voice recognition phone-based interactions – makes authentication an easy task, but it will work considerably better in quiet environments than noisy ones – such as a train stations or with many people talking in the background. As many smartphone users have learnt, fingerprint sensors have a hard time recognising cold and wet fingers. And – as we report in a paper presented at IEEE Identity, Security and Behavior Analysis last week – privacy concerns mean some users ‘don’t like putting their face on the Internet’. Biometrics can’t come soon enough for most users, but there is still a lot of design and testing work to be done to make biometrics work for different interaction, physical and social contexts.

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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