# Local Differential Privacy for Bayesian Optimization

arXiv: https://arxiv.org/abs/2010.06709 · HTML: https://mlsanity.com/paper/2010.06709/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles](https://mlsanity.com/paper/2303.03751/) (2023)
- [Differentially Private Federated Bayesian Optimization with Distributed Exploration](https://mlsanity.com/paper/2110.14153/) (2021)
- [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 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)
- [No-Regret Algorithms for Time-Varying Bayesian Optimization](https://mlsanity.com/paper/2102.06296/) (2021)
- [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)
- [Differentially Private Linear Bandits with Partial Distributed Feedback](https://mlsanity.com/paper/2207.05827/) (2022)
- [Adaptive Control of Differentially Private Linear Quadratic Systems](https://mlsanity.com/paper/2108.11563/) (2021)
- [Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability](https://mlsanity.com/paper/2107.08461/) (2021)
- [Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards](https://mlsanity.com/paper/2306.01121/) (2023)
- [(Private) Kernelized Bandits with Distributed Biased Feedback](https://mlsanity.com/paper/2301.12061/) (2023)
- [Differentially Private Kernelized Contextual Bandits](https://mlsanity.com/paper/2501.07046/) (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)
- [Differentially Private Hyperparameter Tuning using Local Bayesian Optimization](https://mlsanity.com/paper/2502.06044/) (2025)
- [Differential Privacy in Kernelized Contextual Bandits via Random Projections](https://mlsanity.com/paper/2507.13639/) (2025)

## Abstract (arXiv)

Motivated by the increasing concern about privacy in nowadays data-intensive online learning systems, we consider a black-box optimization in the nonparametric Gaussian process setting with local differential privacy (LDP) guarantee. Specifically, the rewards from each user are further corrupted to protect privacy and the learner only has access to the corrupted rewards to minimize the regret. We first derive the regret lower bounds for any LDP mechanism and any learning algorithm. Then, we present three almost optimal algorithms based on the GP-UCB framework and Laplace DP mechanism. In this process, we also propose a new Bayesian optimization (BO) method (called MoMA-GP-UCB) based on median-of-means techniques and kernel approximations, which complements previous BO algorithms for heavy-tailed payoffs with a reduced complexity. Further, empirical comparisons of different algorithms on both synthetic and real-world datasets highlight the superior performance of MoMA-GP-UCB in both private and non-private scenarios.