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SUMMARY:The Privacy of the Analyst and The Power of the State - Naor\, M (
 Weizmann Institute of Science)
DTSTART:20120413T080000Z
DTEND:20120413T090000Z
UID:TALK37463@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Differential privacy is a paradigm in privacy that is aimed at
  mitigating the drawbacks of traditional anonymization techniques\, as it 
 provides a rigorous guarantee for the added risk to an individual in parti
 cipating in a database. Roughly speaking\, a mechanism satisfies different
 ial privacy if for any possible output of the mechanism and any possible s
 et of data on individuals\, the probability of obtaining this particular o
 utput changes only very little with the addition or deletion of the data o
 n an individual. We initiate the study of privacy for the analyst in diffe
 rentially private data analysis. That is\, not only are we concerned with 
 ensuring differential privacy for the data (i.e. individuals or customers)
 \, which are the usual concern of differential privacy\, but we also consi
 der (differential) privacy for the set of queries posed by each data analy
 st. The privacy achieved is with respect to other analysts which are the u
 sers of the system. This problem of analysts' queries being leaked arises 
 only in the context of stateful privacy mechanisms\, in which the response
 s to queries depend on other queries posed. A recent wave of results in th
 e area of differential privacy utilized coordinated noise and state in ord
 er to allow answering hugely many queries. We argue the problem is real by
  proving an exponential gap between the number of queries that can be answ
 ered (with non-trivial error) by stateless and stateful differentially pri
 vate mechanisms. We then give a stateful algorithm for differentially priv
 ate data analysis that also ensures differential privacy for the analyst a
 nd can answer exponentially many queries. Joint work with Cynthia Dwork an
 d Salil Vadhan 
LOCATION:Seminar Room 1\, Newton Institute
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