# Automatic Discovery of Privacy-Utility Pareto Fronts

arXiv: https://arxiv.org/abs/1905.10862 · HTML: https://mlsanity.com/paper/1905.10862/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 16 of 16)

- [The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection](https://mlsanity.com/paper/2111.04906/) (2021)
- [Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization](https://mlsanity.com/paper/2102.04523/) (2021)
- [Efficient Hyperparameter Optimization for Differentially Private Deep Learning](https://mlsanity.com/paper/2108.03888/) (2021)
- [A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design](https://mlsanity.com/paper/2206.13326/) (2022)
- [Deep Learning-based Anonymization of Chest Radiographs: A Utility-preserving Measure for Patient Privacy](https://mlsanity.com/paper/2209.11531/) (2022)
- [On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning](https://mlsanity.com/paper/2205.06720/) (2022)
- [Machine Learning Systems: A Survey from a Data-Oriented Perspective](https://mlsanity.com/paper/2302.04810/) (2023)
- [Regulation Games for Trustworthy Machine Learning](https://mlsanity.com/paper/2402.03540/) (2024)
- [Automated discovery of trade-off between utility, privacy and fairness in machine learning models](https://mlsanity.com/paper/2311.15691/) (2023)
- [SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing](https://mlsanity.com/paper/2211.07218/) (2022)
- [SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation](https://mlsanity.com/paper/2506.19360/) (2025)
- [An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy](https://mlsanity.com/paper/2509.04290/) (2025)
- [Partial sensitivity analysis in differential privacy](https://mlsanity.com/paper/2109.10582/) (2021)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)
- [Differential Privacy in Machine Learning: A Survey from Symbolic AI to LLMs](https://mlsanity.com/paper/2506.11687/) (2025)
- [Optimising for Energy Efficiency and Performance in Machine Learning](https://mlsanity.com/paper/2601.08991/) (2026)

## Abstract (arXiv)

Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off privacy and utility in a principled way. Quantifying this trade-off in advance is essential to decision-makers tasked with deciding how much privacy can be provided in a particular application while maintaining acceptable utility. Analytical utility guarantees offer a rigorous tool to reason about this trade-off, but are generally only available for relatively simple problems. For more complex tasks, such as training neural networks under differential privacy, the utility achieved by a given algorithm can only be measured empirically. This paper presents a Bayesian optimization methodology for efficiently characterizing the privacy--utility trade-off of any differentially private algorithm using only empirical measurements of its utility. The versatility of our method is illustrated on a number of machine learning tasks involving multiple models, optimizers, and datasets.