2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | P.3.4 | ||
| Paper Title | Distribution Privacy Under Function Recoverability | ||
| Authors | Ajaykrishnan Nageswaran, Prakash Narayan, University of Maryland, College Park, United States | ||
| Session | P.3: Information Privacy I | ||
| Presentation | Lecture | ||
| Track | Cryptography, Security and Privacy | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | A user generates $n$ independent and identically distributed data random variables with a probability mass function that must be guarded from a querier. The querier must recover, with a prescribed accuracy, a given function of the data from each of $n$ independent and identically distributed user-devised query responses. The user chooses the data pmf and the random query responses to maximize distribution privacy as gauged by the divergence between the pmf and the querier’s best estimate of it based on the $n$ query responses. A general lower bound is provided for distribution privacy; and, for the case of binary-valued functions, upper and lower bounds that converge to said bound as $n$ grows. Explicit strategies for the user and querier are identified. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
