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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? 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ON-OFF Privacy Against Correlation Over Time
Fangwei Ye, Carolina Naim, Salim El Rouayheb · 2021-04-12 · via cs.IT updates on arXiv.org

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).

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