Blog Archive: 2010

Summer intern position in privacy preserving computation

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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.

Quantum inspired classical results

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In yesterday’s post, I mentioned that one of my favorite topics is classical results informed by the quantum information processing viewpoint. There are now sufficiently many such results that Drucker and deWolf have written a survey, “Quantum Proofs for Classical Theorems.” I was surprised last month, when another such  example popped up in one of the biggest cryptographic results of 2009, Craig Gentry’s discovery of a fully homomorphic encryption scheme.

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Search and/or geometry challenge!

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Some friends of mine believe that “search” has been solved. They explain that they can almost always find what they are looking for, and quickly, using keyword search. My life is much more frustrating! There are all sorts of things I look for and can’t find. An additional source of frustration is that I don’t know when to give up, when to conclude that what I’m looking for isn’t there.

Recently I had this experience with a question I thought would make a good blog challenge:

Does there exist a polyhedron such that all of its faces are nonconvex?

If you can think up a proof or example, please post your answer in the comments section, but with “Spoiler alert:” at its start. If you find an answer through a web search, give us the URL and tell us your search strategy. A URL pointing to discussion of this exact question would also be acceptable, even if the discussion doesn’t provide an answer.

I’d like to give a prize, and thought about various prizes (a Tcho chocolate bar? treating the winner to coffee? …) but decided in true blog spirit to ask for suggestions for an appropriate prize.

P.S. I thought about defining  terms such as “polyhedron” and “nonconvex” here.  But since this is a search and/or geometry challenge, any readers who do not know the meaning of these 3D geometry terms can still participate. I would be particularly delighted if someone who did not understand the question initially was able to find a solution.

Update: An answer has been found. Congratulations, Francine. However I realize I mixed up two searches, and this one isn’t as hard as I thought I remembered.

Recall-oriented search on the web

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Most popular web search engines are optimized for precision—getting that useful document in the in the top five or ten hits so that the user doesn’t have to page through the results to find it. This works well for known-item search (finding an address of a restaurant, a birthday of a movie star, etc.) and for searches that rely on combinations of keywords.

But some kinds of information needs don’t fit that pattern well. Sometimes the information being sought is spread over multiple documents, sometimes people need to find multiple instances of documents that match some query to compare or contrast them, etc. The task becomes more recall- rather than precision-oriented. Furthermore, these searches may be repeated over time, as the user finds information that causes the information need to change. Medical information seeking is one obvious such example. Are there others?

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