ON-OFF Privacy Against Correlation Over Time

TL;DR

This paper addresses the challenge of toggling privacy on and off in user requests over time, modeling user behavior with Markov chains and designing optimal schemes that balance privacy and download efficiency.

Contribution

It introduces a polynomial-time algorithm for constructing optimal ON-OFF privacy schemes and provides tight bounds and LP characterization for their achievable rates.

Findings

Proposed a scheme that is optimal for certain Markov chains.

Derived an upper bound on the achievable download rate.

Presented an LP formulation for the optimal rate.

Abstract

We consider the problem of ON-OFF privacy in which a user is interested in the latest message generated by one of n sources available at a server. The user has the choice to turn privacy ON or OFF depending on whether he wants to hide his interest at the time or not. The challenge of allowing the privacy to be toggled between ON and OFF is that the user's online behavior is correlated over time. Therefore, the user cannot simply ignore the privacy requirement when privacy is OFF. We represent the user's correlated requests by an n-state Markov chain. Our goal is to design ON-OFF privacy schemes with optimal download rate that ensure privacy for past and future requests. We devise a polynomial-time algorithm to construct an ON-OFF privacy scheme. Moreover, we present an upper bound on the achievable rate. We show that the proposed scheme is optimal and the upper bound is tight for some special families of Markov chains. We also give an implicit characterization of the optimal achievable rate as a linear programming (LP).