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The Privacy of the Analyst and The Power of the State

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If you have a question about this talk, please contact Mustapha Amrani.

Semantics and Syntax: A Legacy of Alan Turing

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 participating in a database. Roughly speaking, a mechanism satisfies differential privacy if for any possible output of the mechanism and any possible set of data on individuals, the probability of obtaining this particular output changes only very little with the addition or deletion of the data on an individual. We initiate the study of privacy for the analyst in differentially 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 consider (differential) privacy for the set of queries posed by each data analyst. The privacy achieved is with respect to other analysts which are the users of the system. This problem of analysts’ queries being leaked arises only in the context of stateful privacy mechanisms, in which the responses to queries depend on other queries posed. A recent wave of results in the area of differential privacy utilized coordinated noise and state in order 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 answered (with non-trivial error) by stateless and stateful differentially private mechanisms. We then give a stateful algorithm for differentially private data analysis that also ensures differential privacy for the analyst and can answer exponentially many queries. Joint work with Cynthia Dwork and Salil Vadhan

This talk is part of the Isaac Newton Institute Seminar Series series.

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