Jefferson's Wheel

Our paper on secure stable matching is now available [PDF, 12 pages]:

Jack Doerner, David Evans, abhi shelat. Secure Stable Matching at Scale. 23^rd ACM Conference on Computer and Communications Security (CCS). Vienna, Austria. 24-28 October 2016.

See the OblivC.org site for the code and data. Jack Doerner will present the paper at CCS in October.

Abstract

When a group of individuals and organizations wish to compute a stable matching — for example, when medical students are matched to medical residency programs — they often outsource the computation to a trusted arbiter to preserve the privacy of participants’ preference rankings. Secure multi-party computation presents an alternative that offers the possibility of private matching processes that do not rely on any common trusted third party. However, stable matching algorithms are computationally intensive and involve complex data-dependent memory access patterns, so they have previously been considered infeasible for execution in a secure multiparty context on non-trivial inputs.

We adapt the classic Gale-Shapley algorithm for use in such a context, and show experimentally that our modifications yield a lower asymptotic complexity and more than an order of magnitude in practical cost improvement over previous techniques. Our main insights are to design new oblivious data structures that exploit the properties of the matching algorithms. We then apply our secure computation techniques to the instability chaining algorithm of Roth and Peranson, currently in use by the National Resident Matching Program. The resulting algorithm is efficient enough to be useful at the scale required for matching medical residents nationwide, taking just over 17 hours to complete an execution simulating the 2016 NRMP match with more than 35,000 participants and 30,000 residency slots.

Great to visit our former student Joseph Calandrino at the Federal Trade Commission in DC, where he is now a Research Director.

Denis Nekipelov and I gave a joint talk there about using secure multi-party computation techniques to enable data analyses across sensitive, divided data sets in the room where the FTC commissioners meet.

Denis Nekipelov, Joseph Calandrino, David Evans, Devesh Ravel

I’m co-organizing a workshop to be held in conjunction with NIPS on Private Multi‑Party Machine Learning, along with Borja Balle, Aurélien Bellet, Adrià Gascón. The one-day workshop will be held Dec 9 or Dec 10 in Barcelona.

NIPS workshops are different from typical workshops attached to computer security conferences, with lots of invited talks (and we have some great speakers lined up for PMPML16), but there is also an opportunity for researchers to submit short papers to be presented at the workshop either as short talks or posters.

Hannah Li presented a poster at USENIX Security Symposium on how popular web frameworks perform authentication.

Insecure by Default? Authentication Services in Popular Web Frameworks
[Abstract (PDF)] [Poster (PDF)]

The work studies how different design choices made by web frameworks impact the security of web applications built by typical developers using those frameworks, with a goal of understanding the usability and performance trade-offs that lead frameworks to adopt insecure defaults, and develop alternatives that lead to better security without sacrificing the needs of easy initial development and deployment.