Allerton 2015 Paper Abstract

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Paper ThA5.5

Moraffah, Bahman (Arizona State University), Sankar, Lalitha (Arizona State University)

Information-Theoretic Private Interactive Mechanism

Scheduled for presentation during the Invited Session "Security Adversarial Machine Learning" (ThA5), Thursday, October 1, 2015, 09:50−10:10, Lower Level

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on November 19, 2019

Keywords Information Theory, Distributed Computation on Networks, Security and Trust

Abstract

An information-theoretic mechanism for privacy-guaranteed interactions is introduced between two memoryless correlated sources where each source is characterized by a pair of public and private variables. The interactions are modeled as a collection of $K/2$ pairs of random mappings, one pair for each of the $K$ rounds of interactions. The $K/2$ random mapping pairs are chosen jointly to minimize the information leakage (privacy measure) over $K$ rounds of the private variable of each source at the other source while ensuring that a desired measure of utility (distortion) of the revealed public variable is satisfied. Arguing that an average case information-theoretic privacy metric can be appropriate for streaming data settings, this paper shows that in general, interaction reduces privacy leakage by drawing some parallels between this problem and the classic interactive source coding problem. Specifically, for the log-loss distortion metric it is shown that the resulting interaction problem is an analog of an interactive information bottleneck problem for which a one-shot interactive mechanism is, in general, not optimal. For the resulting problem with a non-convex constraint space, an algorithm that extends the one-way agglomerative information bottleneck algorithm to the interactive setting is introduced.

 

 

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