This is the third of a series of posts advertising internship positions at FXPAL for the summer of 2010.  A listing of all blog posts about our 2010 internship positions is available here.

Significant privacy issues arise when personal data is stored and analyzed. This issue is exacerbated when part or all of the storage and analysis is outsourced to a third party. To support such analysis in an awareness system, while addressing the privacy concerns, we are building into our system a facility that supports computation of simple statistics on encrypted data. This facility can be extended in a number of ways to support a greater variety of computations. There are a wealth of research questions related to designing such a system to support the types of computations useful to our application while choosing the best tradeoffs in terms of storage, bandwidth, division of labor between the third party and the clients, computation time at encryption, time to compute the statistics, and time to decrypt.

Prospective candidates should be enrolled in a PhD program and have significant experience in privacy and security, particularly computation on or search of encrypted data.

The intern will be hosted by Eleanor Rieffel.  For more information on the FXPAL internship program, please visit our web site.

Almost 10 years ago, L. Sweeny published an analysis of summary census data that was used to identify 87% of respondents based only on their ZIP code, gender, and date of birth, data that we all think of (and the census treats as) relatively anonymous. At about the same time, I visited a friend at a large consulting firm who demonstrated data mining software that combined data from multiple sources and was able to discover many facts about people, that while not particularly revealing individually, painted a much more complete picture when federated. Now comes the news (thanks Daniel) that a group at MIT was able to make better-than-chance predictions about people’s sexual orientation using Facebook friends as training data. Whereas the census analysis and the data mining tools could be considered academic exercises on datasets to which most people don’t have access, the MIT results have much more immediate and potentially damaging implications.

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