mlsanityData to 7 Oct 2026

Differentially Private Bayesian Inference for Exponential Families

arXiv 1809.02188 ↗First posted 6 Sep 2018cs.LG cs.CR stat.MLcited by 18

Not on the map. Why?

Abstract (arXiv)

The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources. We present the first method for private Bayesian inference in exponential families that properly accounts for noise introduced by the privacy mechanism. It is efficient because it works only with sufficient statistics and not individual data. Unlike other methods, it gives properly calibrated posterior beliefs in the non-asymptotic data regime.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 3; 2024: 2; 2025: 3; 2026: 2.

18 papers.

Sort:

  1. 2024Incentives in Private Collaborative Machine Learningcited by 10
  2. 2022Noise-Aware Statistical Inference with Differentially Private Synthetic Datacited by 9
  3. 2021Non-parametric Differentially Private Confidence Intervals for the Mediancited by 7
  4. 2022Statistic Selection and MCMC for Differentially Private Bayesian Estimationcited by 3
  5. 2021Dirichlet Mechanism for Differentially Private KL Divergence Minimizationcited by 2
  6. 2022Differentially private partitioned variational inferencecited by 2
  7. 2022DPVIm: Differentially Private Variational Inference Improvedcited by 2
  8. 2021Locally Differentially Private Bayesian Inferencecited by 1
  9. 2022Decentralized Collaborative Learning with Probabilistic Data Protectioncited by 1
  10. 2023Differentially Private Online Bayesian Estimation With Adaptive Truncationcited by 1
  11. 2023Simulation-based Bayesian Inference from Privacy Protected Datacited by 1
  12. 2025Private Statistical Estimation via Truncationcited by 1
6 more
  1. 2025Fiducial Matching: Differentially Private Inference for Categorical Datacited by 1
  2. 2026Computation-Utility-Privacy Tradeoffs in Bayesian Estimationcited by 1
  3. 2023Differentially Private Statistical Inference through β-Divergence One Posterior Samplingcited by 0
  4. 2024Noise-Aware Differentially Private Variational Inferencecited by 0
  5. 2025Optimal Survey Design for Private Mean Estimationcited by 0
  6. 2026Noise-Calibrated Inference from Differentially Private Sufficient Statistics in Exponential Familiescited by 0