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Multi-Armed Bandits with Local Differential Privacy

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arxiv 2007.03121 v1 pith:JKX7LVVR submitted 2020-07-06 cs.LG cs.CRstat.ML

Wenbo Ren , Xingyu Zhou , Jia Liu , Ness B. Shroff This is my paper
classification cs.LGcs.CRstat.ML
keywords differentiallowerprivacyregretactivitiesagentalgorithmsbandit
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Faster Rates for Private Adversarial Bandits

    cs.LG 2025-05 conditional novelty 8.0 of 10

    By batching losses and using heavy-tailed bandit algorithms, any non-private adversarial bandit algorithm can be made epsilon-differentially private with regret O(sqrt(KT)/sqrt(epsilon)), and the first private expert-...

  2. Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment

    cs.LG 2025-05 conditional novelty 6.0 of 10

    SquareχPO, a square-loss variant of χPO, achieves optimal 1/sqrt(n) suboptimality under label privacy and Huber corruption for offline direct alignment with general function classes.

  3. Locally Differentially Private Thresholding Bandits

    cs.LG 2025-07 conditional novelty 5.0 of 10

    Locally private thresholding bandit algorithms achieve near-optimal error and sample complexity, matching new lower bounds for small privacy budgets.

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