Multi-Armed Bandits with Local Differential Privacy

Wenbo Ren · Xingyu Zhou · Jia Liu · Ness B. Shroff

28 citations0/0 files storedarXiv (Cornell University)

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Advanced Bandit Algorithms Research Privacy-Preserving Technologies in Data Reinforcement Learning in Robotics

Abstract

This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee. In stochastic bandit systems, the rewards may refer to the users' activities, which may involve private information and the users may not want the agent to know. However, in many cases, the agent needs to know these activities to provide better services such as recommendations and news feeds. To handle this dilemma, we adopt differential privacy and study the regret upper and lower bounds for MAB algorithms with a given LDP guarantee. In this paper, we prove a lower bound and propose algorithms whose regret upper bounds match the lower bound up to constant factors. Numerical experiments also confirm our conclusions.

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2002Finite-time Analysis of the Multiarmed Bandit Problem 2011Analysis of Thompson Sampling for the multi-armed bandit problem 2016Algorithms for Differentially Private Multi-Armed Bandits 2018Differentially Private Contextual Linear Bandits 2015(Nearly) optimal differentially private stochastic multi-arm bandits

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