[Busec] Fwd: CS/RISCS Seminar Friday, May 6, 3:30 - Aaron Roth on Selling Privacy at Auction
goldbe at cs.bu.edu
Tue May 3 15:06:55 EDT 2011
---------- Forwarded message ----------
From: Steve Homer <homer at cs.bu.edu>
Date: Tue, May 3, 2011 at 1:58 PM
Subject: CS/RISCS Seminar Friday, May 6, 3:30 - Aaron Roth on Selling
Privacy at Auction
To: Steve Homer <homer at cs.bu.edu>
Computer Science Department and BU RISCS Center
Selling Privacy at Auction
Microsoft Research, New England
Friday, May 6, 3:30 PM
The talk will take place at BU at 111 Cummington Street
in room 135.
In this talk, we will consider the problem of setting up markets for
private data, though the lens of differential privacy. Specifically,
we consider a setting in which a data analyst wishes to buy
information from a population from which he can estimate some
statistic. The analyst wishes to obtain an accurate estimate cheaply.
On the other hand, the owners of the private data experience some cost
for their loss of privacy, and must be compensated for this loss.
Agents are selfish, and wish to maximize their profit, so our goal is
to design truthful mechanisms.
Our main result is that such auctions can naturally be viewed and
optimally solved as variants of multi-unit procurement auctions. Based
on this result, we derive auctions for two natural settings which are
optimal up to small constant factors:
1) In the setting in which the data analyst has a fixed accuracy goal,
we show that an application of the classic Vickrey auction achieves
the analyst's accuracy goal while minimizing his total payment.
2) In the setting in which the data analyst has a fixed budget, we
give a mechanism which maximizes the accuracy of the resulting
estimate while guaranteeing that the resulting sum payments do not
exceed the analysts budget.
In both of these results, we ignore the privacy cost due to possible
correlations between an individuals private data and his valuation for
privacy itself. We then show that no individually rational mechanism
can compensate individuals for the privacy loss incurred due to their
reported valuations for privacy. This is nevertheless an important
issue, and modeling it correctly is one of the many exciting
directions for future work.
This talk is based on joint work with Arpita Ghosh
Computer Science, Boston University
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