# Locally Differentially Private (Contextual) Bandits Learning

arXiv: https://arxiv.org/abs/2006.00701 · HTML: https://mlsanity.com/paper/2006.00701/

## Key facts

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

## Built on by (most cited first, 30 of 53)

- [Large Scale Private Learning via Low-rank Reparametrization](https://mlsanity.com/paper/2106.09352/) (2021)
- [Optimal Order Simple Regret for Gaussian Process Bandits](https://mlsanity.com/paper/2108.09262/) (2021)
- [Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles](https://mlsanity.com/paper/2303.03751/) (2023)
- [When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits](https://mlsanity.com/paper/2209.02570/) (2022)
- [Shuffle Private Linear Contextual Bandits](https://mlsanity.com/paper/2202.05567/) (2022)
- [Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits](https://mlsanity.com/paper/2106.02575/) (2021)
- [Differentially Private Multi-Armed Bandits in the Shuffle Model](https://mlsanity.com/paper/2106.02900/) (2021)
- [Differentially Private Regret Minimization in Episodic Markov Decision Processes](https://mlsanity.com/paper/2112.10599/) (2021)
- [Differentially Private Reinforcement Learning with Linear Function Approximation](https://mlsanity.com/paper/2201.07052/) (2022)
- [Offline Reinforcement Learning with Differential Privacy](https://mlsanity.com/paper/2206.00810/) (2022)
- [Differentially Private Stochastic Linear Bandits: (Almost) for Free](https://mlsanity.com/paper/2207.03445/) (2022)
- [Privacy Amplification via Shuffling for Linear Contextual Bandits](https://mlsanity.com/paper/2112.06008/) (2021)
- [Near-Optimal Differentially Private Reinforcement Learning](https://mlsanity.com/paper/2212.04680/) (2022)
- [Distributed Differential Privacy in Multi-Armed Bandits](https://mlsanity.com/paper/2206.05772/) (2022)
- [On Differentially Private Federated Linear Contextual Bandits](https://mlsanity.com/paper/2302.13945/) (2023)
- [Differential Privacy in Personalized Pricing with Nonparametric Demand Models](https://mlsanity.com/paper/2109.04615/) (2021)
- [Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes](https://mlsanity.com/paper/2110.10133/) (2021)
- [Federated Linear Contextual Bandits with User-level Differential Privacy](https://mlsanity.com/paper/2306.05275/) (2023)
- [Differentially Private Exploration in Reinforcement Learning with Linear Representation](https://mlsanity.com/paper/2112.01585/) (2021)
- [Federated Online Clustering of Bandits](https://mlsanity.com/paper/2208.14865/) (2022)
- [Differentially Private Linear Bandits with Partial Distributed Feedback](https://mlsanity.com/paper/2207.05827/) (2022)
- [Dynamic Global Sensitivity for Differentially Private Contextual Bandits](https://mlsanity.com/paper/2208.14555/) (2022)
- [Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards](https://mlsanity.com/paper/2306.01121/) (2023)
- [Differentially Private Kernelized Contextual Bandits](https://mlsanity.com/paper/2501.07046/) (2025)
- [KL-regularization Itself is Differentially Private in Bandits and RLHF](https://mlsanity.com/paper/2505.18407/) (2025)
- [SquarechiPO: Differentially Private and Robust chi2-Preference Optimization in Offline Direct Alignment](https://mlsanity.com/paper/2505.21395/) (2025)
- [Concurrent Shuffle Differential Privacy Under Continual Observation](https://mlsanity.com/paper/2301.12535/) (2023)
- [Fixed-Budget Differentially Private Best Arm Identification](https://mlsanity.com/paper/2401.09073/) (2024)
- [On the Optimal Regret of Locally Private Linear Contextual Bandit](https://mlsanity.com/paper/2404.09413/) (2024)
- [DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2308.09902/) (2023)

## 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.