Encrypted Linear Contextual Bandit

TL;DR

This paper introduces a privacy-preserving linear contextual bandit algorithm that uses homomorphic encryption to perform online learning without decrypting user data, maintaining privacy while achieving competitive regret bounds.

Contribution

It presents the first homomorphic encryption-based framework for linear contextual bandits, enabling privacy-preserving online decision-making with theoretical regret guarantees.

Findings

Achieves $ ilde{O}(d ext{--} ext{} oot{T}{})$ regret bound with encrypted data.

Demonstrates feasibility of privacy-preserving bandits in practical applications.

Maintains data privacy without sacrificing theoretical performance.

Abstract

Contextual bandit is a general framework for online learning in sequential decision-making problems that has found application in a wide range of domains, including recommendation systems, online advertising, and clinical trials. A critical aspect of bandit methods is that they require to observe the contexts --i.e., individual or group-level data-- and rewards in order to solve the sequential problem. The large deployment in industrial applications has increased interest in methods that preserve the users' privacy. In this paper, we introduce a privacy-preserving bandit framework based on homomorphic encryption{\color{violet} which allows computations using encrypted data}. The algorithm \textit{only} observes encrypted information (contexts and rewards) and has no ability to decrypt it. Leveraging the properties of homomorphic encryption, we show that despite the complexity of the setting, it is possible to solve linear contextual bandits over encrypted data with a $\widetilde{O}(d\sqrt{T})$ regret bound in any linear contextual bandit problem, while keeping data encrypted.