# Privacy-Preserving Bandits

arXiv: https://arxiv.org/abs/1909.04421 · HTML: https://mlsanity.com/paper/1909.04421/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits](https://mlsanity.com/paper/2106.02575/) (2021)
- [On-device Federated Learning with Flower](https://mlsanity.com/paper/2104.03042/) (2021)
- [adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems](https://mlsanity.com/paper/2303.04257/) (2023)
- [Differentially Private Federated Combinatorial Bandits with Constraints](https://mlsanity.com/paper/2206.13192/) (2022)
- [Vertical Federated Linear Contextual Bandits](https://mlsanity.com/paper/2210.11050/) (2022)
- [Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit Study](https://mlsanity.com/paper/2310.00221/) (2023)

## Abstract (arXiv)

Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users. Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's privacy, however, the agent requires longer to produce useful recommendations, as it does not leverage feedback from other users. This paper proposes a technique we call Privacy-Preserving Bandits (P2B); a system that updates local agents by collecting feedback from other local agents in a differentially-private manner. Comparisons of our proposed approach with a non-private, as well as a fully-private (local) system, show competitive performance on both synthetic benchmarks and real-world data. Specifically, we observed only a decrease of 2.6% and 3.6% in multi-label classification accuracy, and a CTR increase of 0.0025 in online advertising for a privacy budget ε ≈ 0.693. These results suggest P2B is an effective approach to challenges arising in on-device privacy-preserving personalization.