# Differentially Private Bayesian Learning on Distributed Data

arXiv: https://arxiv.org/abs/1703.01106 · HTML: https://mlsanity.com/paper/1703.01106/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2017.

## Built on by (most cited first, 10 of 10)

- [Survey: Leakage and Privacy at Inference Time](https://mlsanity.com/paper/2107.01614/) (2021)
- [One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy](https://mlsanity.com/paper/2203.06580/) (2022)
- [Non-parametric Differentially Private Confidence Intervals for the Median](https://mlsanity.com/paper/2106.10333/) (2021)
- [Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability](https://mlsanity.com/paper/2107.08461/) (2021)
- [Differentially private partitioned variational inference](https://mlsanity.com/paper/2209.11595/) (2022)
- [Decentralized Collaborative Learning with Probabilistic Data Protection](https://mlsanity.com/paper/2208.10674/) (2022)
- [Differentially Private Online Bayesian Estimation With Adaptive Truncation](https://mlsanity.com/paper/2301.08202/) (2023)
- [Differentially Private Distributed Bayesian Linear Regression with MCMC](https://mlsanity.com/paper/2301.13778/) (2023)
- [On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps](https://mlsanity.com/paper/2407.19286/) (2024)
- [Federated and differentially private estimation of KL divergence](https://mlsanity.com/paper/2411.16478/) (2024)

## Abstract (arXiv)

Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a learning strategy based on a secure multi-party sum function for aggregating summaries from data holders and the Gaussian mechanism for DP. Our method builds on an asymptotically optimal and practically efficient DP Bayesian inference with rapidly diminishing extra cost.