DIMACS Workshop on Differential Privacy across Computer Science
October 24-26, 2012
(immediately after FOCS 2012)
Call for Abstracts — Short Presentations
The upcoming DIMACS workshop on differential privacy will feature invited talks by experts from a range of areas in computer science as well as short talks (5 to 10 minutes) by participants.
Participants interested in giving a short presentation should send an email to email@example.com containing a proposed talk title, abstract, and the speaker’s name and affiliation. We will try to
accommodate as many speakers as possible, but
a) requests received before October 1 will get full consideration
b) priority will be given to junior researchers, so students and postdocs should indicate their status in the email.
More information about the workshop:
The last few years have seen an explosion of results concerning differential privacy across many distinct but overlapping communities in computer science: Theoretical Computer Science, Databases, Programming Languages, Machine Learning, Data Mining, Security, and Cryptography. Each of these different areas has different priorities and techniques, and despite very similar interests, motivations, and choice of problems, it has become difficult to keep track of this large literature across so many different venues. The purpose of this workshop is to bring researchers in differential privacy across all of these communities together under one roof to discuss recent results and synchronize our understanding of the field. The first day of the workshop will include tutorials, representing a broad cross-section of research across fields. The remaining days will be devoted to talks on the exciting recent results in differential privacy across communities, discussion and formation of interesting open problems, and directions for potential inter-community collaborations.
Research often takes twisty little paths, and as the result of a recent attempt to gain understanding about a problem I was trying to understand the difference between the following two systems with balls and (ordered) bins:
- Synchronous: take all of the top balls in each bin and reassign them randomly and uniformly to the bottoms of the bins.
- Asynchronous: pick a random bin, take the top ball in that bin, and reassign it randomly and uniformly to the bottom of a bin.
These processes sound a bit similar, right? The first one is a batch version of the second one. Sort of. We can think of this as modeling customers (balls) in queues (bins) or balls being juggled by hands (bins).
Each of these processes can be modeled as a Markov chain on the vector of bin occupation numbers. For example, for 3 balls and 3 bins we have configurations that look like (3,0,0) and its permutations, (2,1,0) and its permutations, and (1,1,1) for a total of 10 states. If you look at the two Markov chains, they are different, and it turns out they have different stationary distributions, even. Why is that? The asynchronous chain is reversible and all transitions are symmetric. The synchronous one is not reversible.
One question is if there is a limiting sense in which these are similar — can the synchronous batch-recirculating scheme be approximated by the asynchronous version if we let or get very large?