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SUMMARY:Buying Private Data without Verification - Katrina Ligett (Hebrew 
 University of Jerusalem\; CALTECH (California Institute of Technology))
DTSTART:20161028T133000Z
DTEND:20161028T143000Z
UID:TALK68658@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Joint work with Arpita Ghosh\, Aaron Roth\, and Grant Schoeneb
 eck<br><br>We consider&nbsp\;the&nbsp\; problem&nbsp\; of&nbsp\; designing
 &nbsp\; a&nbsp\; survey&nbsp\; to&nbsp\; aggregate&nbsp\; non-verifiable&n
 bsp\; information&nbsp\; from a privacy-sensitive population: an analyst w
 ants to compute some aggregate statistic from the private bits held by eac
 h member of a population\, but cannot verify the correctness of the bits r
 eported by participants in his survey. Individuals in the population are s
 trategic agents with a cost for privacy\, i.e.\, they not only account for
  the payments they expect to receive from the mechanism\, but also their p
 rivacy costs from any information revealed about them by the mechanism&rsq
 uo\;s outcome&mdash\;the computed statistic as well as the payments&mdash\
 ;to determine their utilities. How can the analyst design payments to obta
 in an accurate estimate of the population statistic when individuals strat
 egically decide both whether to participate and whether to truthfully repo
 rt their sensitive information?  <br><span><br>In this talk\, we will disc
 uss an approach to this problem based on ideas from peer prediction and di
 fferential privacy.</span>
LOCATION:Seminar Room 1\, Newton Institute
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