Federated linear contextual bandits with user-level differential privacy
This paper studies federated linear contextual bandits under the notion of user-level
differential privacy (DP). We first introduce a unified federated bandits framework that can …
differential privacy (DP). We first introduce a unified federated bandits framework that can …
Locally differentially private (contextual) bandits learning
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 …
simple black-box reduction frameworks that can solve a large family of context-free bandits …
When privacy meets partial information: A refined analysis of differentially private bandits
We study the problem of multi-armed bandits with ε-global Differential Privacy (DP). First, we
prove the minimax and problem-dependent regret lower bounds for stochastic and linear …
prove the minimax and problem-dependent regret lower bounds for stochastic and linear …
Local differential privacy for regret minimization in reinforcement learning
E Garcelon, V Perchet… - Advances in Neural …, 2021 - proceedings.neurips.cc
Reinforcement learning algorithms are widely used in domains where it is desirable to
provide a personalized service. In these domains it is common that user data contains …
provide a personalized service. In these domains it is common that user data contains …
Privacy-preserving q-learning with functional noise in continuous spaces
We consider differentially private algorithms for reinforcement learning in continuous
spaces, such that neighboring reward functions are indistinguishable. This protects the …
spaces, such that neighboring reward functions are indistinguishable. This protects the …
Differentially private multi-armed bandits in the shuffle model
Abstract We give an $(\varepsilon,\delta) $-differentially private algorithm for the Multi-Armed
Bandit (MAB) problem in the shuffle model with a distribution-dependent regret of $ O\left …
Bandit (MAB) problem in the shuffle model with a distribution-dependent regret of $ O\left …
Differentially private reinforcement learning with linear function approximation
X Zhou - Proceedings of the ACM on Measurement and Analysis …, 2022 - dl.acm.org
Motivated by the wide adoption of reinforcement learning (RL) in real-world personalized
services, where users' sensitive and private information needs to be protected, we study …
services, where users' sensitive and private information needs to be protected, we study …
On differentially private federated linear contextual bandits
X Zhou, SR Chowdhury - arXiv preprint arXiv:2302.13945, 2023 - arxiv.org
We consider cross-silo federated linear contextual bandit (LCB) problem under differential
privacy, where multiple silos (agents) interact with the local users and communicate via a …
privacy, where multiple silos (agents) interact with the local users and communicate via a …
Optimal rates of (locally) differentially private heavy-tailed multi-armed bandits
In this paper we investigate the problem of stochastic multi-armed bandits (MAB) in the
(local) differential privacy (DP/LDP) model. Unlike previous results that assume …
(local) differential privacy (DP/LDP) model. Unlike previous results that assume …
Shuffle private linear contextual bandits
SR Chowdhury, X Zhou - arXiv preprint arXiv:2202.05567, 2022 - arxiv.org
Differential privacy (DP) has been recently introduced to linear contextual bandits to formally
address the privacy concerns in its associated personalized services to participating users …
address the privacy concerns in its associated personalized services to participating users …