# Thompson Sampling under Bernoulli Rewards with Local Differential Privacy

arXiv: https://arxiv.org/abs/2307.00863 · HTML: https://mlsanity.com/paper/2307.00863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Multi-Armed Bandits with Local Differential Privacy](https://mlsanity.com/paper/2007.03121/) (2020)
- [Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?](https://mlsanity.com/paper/1905.12298/) (2019)

## Abstract (arXiv)

This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee. Given a fixed privacy budget ε, we consider three privatizing mechanisms under Bernoulli scenario: linear, quadratic and exponential mechanisms. Under each mechanism, we derive stochastic regret bound for Thompson Sampling algorithm. Finally, we simulate to illustrate the convergence of different mechanisms under different privacy budgets.