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Locally Differentially Private (Contextual) Bandits Learning

arXiv 2006.00701 ↗First posted 1 Jun 2020cs.LG stat.MLcited by 53

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Abstract (arXiv)

We study locally differentially private (LDP) bandits learning in this paper. First, we propose simple black-box reduction frameworks that can solve a large family of context-free bandits learning problems with LDP guarantee. Based on our frameworks, we can improve previous best results for private bandits learning with one-point feedback, such as private Bandits Convex Optimization, and obtain the first result for Bandits Convex Optimization (BCO) with multi-point feedback under LDP. LDP guarantee and black-box nature make our frameworks more attractive in real applications compared with previous specifically designed and relatively weaker differentially private (DP) context-free bandits algorithms. Further, we extend our (varepsilon, δ)-LDP algorithm to Generalized Linear Bandits, which enjoys a sub-linear regret tildeO(T3/4/varepsilon) and is conjectured to be nearly optimal. Note that given the existing Omega(T) lower bound for DP contextual linear bandits (Shariff & Sheffe, 2018), our result shows a fundamental difference between LDP and DP contextual bandits learning.

Key facts. Cited by 53 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jun 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 10; 2022: 12; 2023: 8; 2024: 7; 2025: 11; 2026: 5.

53 papers.

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  1. 2021Large Scale Private Learning via Low-rank Reparametrizationcited by 88
  2. 2021Optimal Order Simple Regret for Gaussian Process Banditscited by 54
  3. 2023Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oraclescited by 35
  4. 2022When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Banditscited by 28
  5. 2022Shuffle Private Linear Contextual Banditscited by 27
  6. 2021Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Banditscited by 25
  7. 2021Differentially Private Multi-Armed Bandits in the Shuffle Modelcited by 24
  8. 2021Differentially Private Regret Minimization in Episodic Markov Decision Processescited by 23
  9. 2022Differentially Private Reinforcement Learning with Linear Function Approximationcited by 22
  10. 2022Offline Reinforcement Learning with Differential Privacycited by 20
  11. 2022Differentially Private Stochastic Linear Bandits: (Almost) for Freecited by 19
  12. 2021Privacy Amplification via Shuffling for Linear Contextual Banditscited by 17
38 more
  1. 2022Near-Optimal Differentially Private Reinforcement Learningcited by 16
  2. 2022Distributed Differential Privacy in Multi-Armed Banditscited by 14
  3. 2023On Differentially Private Federated Linear Contextual Banditscited by 14
  4. 2021Differential Privacy in Personalized Pricing with Nonparametric Demand Modelscited by 13
  5. 2021Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processescited by 13
  6. 2023Federated Linear Contextual Bandits with User-level Differential Privacycited by 12
  7. 2021Differentially Private Exploration in Reinforcement Learning with Linear Representationcited by 11
  8. 2022Federated Online Clustering of Banditscited by 10
  9. 2022Differentially Private Linear Bandits with Partial Distributed Feedbackcited by 8
  10. 2022Dynamic Global Sensitivity for Differentially Private Contextual Banditscited by 5
  11. 2023Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewardscited by 4
  12. 2025Differentially Private Kernelized Contextual Banditscited by 3
  13. 2025KL-regularization Itself is Differentially Private in Bandits and RLHFcited by 3
  14. 2025SquarechiPO: Differentially Private and Robust chi2-Preference Optimization in Offline Direct Alignmentcited by 3
  15. 2023Concurrent Shuffle Differential Privacy Under Continual Observationcited by 2
  16. 2024Fixed-Budget Differentially Private Best Arm Identificationcited by 2
  17. 2024On the Optimal Regret of Locally Private Linear Contextual Banditcited by 2
  18. 2023DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learningcited by 1
  19. 2024Privacy Preserving Adaptive Experiment Designcited by 1
  20. 2024FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Banditscited by 1
  21. 2024Privacy Preserving Reinforcement Learning for Population Processescited by 1
  22. 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-upscited by 1
  23. 2025Faster Rates for Private Adversarial Banditscited by 1
  24. 2025Online Linear Regression with Paid Stochastic Featurescited by 1
  25. 2026Shuffle and Joint Differential Privacy for Generalized Linear Contextual Banditscited by 1
  26. 2021Encrypted Linear Contextual Banditcited by 0
  27. 2022Learning in Distributed Contextual Linear Bandits Without Sharing the Contextcited by 0
  28. 2022Vertical Federated Linear Contextual Banditscited by 0
  29. 2023Local Differential Privacy for Sequential Decision Making in a Changing Environmentcited by 0
  30. 2023Robust and differentially private stochastic linear banditscited by 0
  31. 2024Differentially Private High Dimensional Banditscited by 0
  32. 2025Private Selection with Heterogeneous Sensitivitiescited by 0
  33. 2025Locally Private Nonparametric Contextual Multi-armed Banditscited by 0
  34. 2025The Safety-Privacy Tradeoff in Linear Banditscited by 0
  35. 2025Optimal Regret of Bernoulli Bandits under Global Differential Privacycited by 0
  36. 2025Differential Privacy in Kernelized Contextual Bandits via Random Projectionscited by 0
  37. 2025Beyond ATE: Multi-Criteria Design for A/B Testingcited by 0
  38. 2026Unlearning Offline Stochastic Multi-Armed Banditscited by 0

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