# Differentially private stochastic expectation propagation (DP-SEP)

arXiv: https://arxiv.org/abs/2111.13219 · HTML: https://mlsanity.com/paper/2111.13219/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [An Introduction to Matrix Concentration Inequalities](https://mlsanity.com/paper/1501.01571/) (2015)
- [Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks](https://mlsanity.com/paper/1502.05336/) (2015)
- [Subsampled R'enyi Differential Privacy and Analytical Moments Accountant](https://mlsanity.com/paper/1808.00087/) (2018)
- [Differentially Private Learning with Adaptive Clipping](https://mlsanity.com/paper/1905.03871/) (2019)
- [Expectation Propagation for approximate Bayesian inference](https://mlsanity.com/paper/1301.2294/) (2013)
- [Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo](https://mlsanity.com/paper/1502.07645/) (2015)
- [On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis](https://mlsanity.com/paper/1603.07294/) (2016)
- [Stochastic Expectation Propagation](https://mlsanity.com/paper/1506.04132/) (2015)
- [Bayesian Differential Privacy through Posterior Sampling](https://mlsanity.com/paper/1306.1066/) (2013)
- [Differentially Private Variational Inference for Non-conjugate Models](https://mlsanity.com/paper/1610.08749/) (2016)
- [On Connecting Stochastic Gradient MCMC and Differential Privacy](https://mlsanity.com/paper/1712.09097/) (2017)
- [Differentially Private Bayesian Inference for Generalized Linear Models](https://mlsanity.com/paper/2011.00467/) (2020)
- [Variational Bayes In Private Settings (VIPS)](https://mlsanity.com/paper/1611.00340/) (2016)
- [On the Differential Privacy of Bayesian Inference](https://mlsanity.com/paper/1512.06992/) (2015)

## Built on by (most cited first, 1 of 1)

- [Differentially private partitioned variational inference](https://mlsanity.com/paper/2209.11595/) (2022)

## Abstract (arXiv)

We are interested in privatizing an approximate posterior inference algorithm called Expectation Propagation (EP). EP approximates the posterior by iteratively refining approximations to the local likelihoods, and is known to provide better posterior uncertainties than those by variational inference (VI). However, EP needs a large memory to maintain all local approximates associated with each datapoint in the training data. To overcome this challenge, stochastic expectation propagation (SEP) considers a single unique local factor that captures the average effect of each likelihood term to the posterior and refines it in a way analogous to EP. In terms of privacy, SEP is more tractable than EP because at each refining step of a factor, the remaining factors are fixed and do not depend on other datapoints as in EP, which makes the sensitivity analysis straightforward. We provide a theoretical analysis of the privacy-accuracy trade-off in the posterior estimates under our method, called differentially private stochastic expectation propagation (DP-SEP). Furthermore, we demonstrate the performance of our DP-SEP algorithm evaluated on both synthetic and real-world datasets in terms of the quality of posterior estimates at different levels of guaranteed privacy.