# Differentially Private Bayesian Optimization

arXiv: https://arxiv.org/abs/1501.04080 · HTML: https://mlsanity.com/paper/1501.04080/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2015.

## Built on by (most cited first, 12 of 12)

- [Differentially Private Federated Bayesian Optimization with Distributed Exploration](https://mlsanity.com/paper/2110.14153/) (2021)
- [The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection](https://mlsanity.com/paper/2111.04906/) (2021)
- [Accuracy, Interpretability, and Differential Privacy via Explainable Boosting](https://mlsanity.com/paper/2106.09680/) (2021)
- [No-Regret Algorithms for Private Gaussian Process Bandit Optimization](https://mlsanity.com/paper/2102.12467/) (2021)
- [On Noisy Evaluation in Federated Hyperparameter Tuning](https://mlsanity.com/paper/2212.08930/) (2022)
- [A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm](https://mlsanity.com/paper/2210.08295/) (2022)
- [Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review](https://mlsanity.com/paper/2306.00687/) (2023)
- [Differentially Private Kernelized Contextual Bandits](https://mlsanity.com/paper/2501.07046/) (2025)
- [Differentially Private Hyperparameter Tuning using Local Bayesian Optimization](https://mlsanity.com/paper/2502.06044/) (2025)
- [Locally Private Nonparametric Contextual Multi-armed Bandits](https://mlsanity.com/paper/2503.08098/) (2025)
- [Differential Privacy in Kernelized Contextual Bandits via Random Projections](https://mlsanity.com/paper/2507.13639/) (2025)
- [ADKO: Agentic Decentralized Knowledge Optimization](https://mlsanity.com/paper/2605.07863/) (2026)

## Abstract (arXiv)

Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide variety of machine learning models. The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems. As machine learning becomes commonplace, Bayesian optimization becomes an attractive method for practitioners to automate the process of classifier hyper-parameter tuning. A key observation is that the data used for tuning models in these settings is often sensitive. Certain data such as genetic predisposition, personal email statistics, and car accident history, if not properly private, may be at risk of being inferred from Bayesian optimization outputs. To address this, we introduce methods for releasing the best hyper-parameters and classifier accuracy privately. Leveraging the strong theoretical guarantees of differential privacy and known Bayesian optimization convergence bounds, we prove that under a GP assumption these private quantities are also near-optimal. Finally, even if this assumption is not satisfied, we can use different smoothness guarantees to protect privacy.