mlsanityData to 14 Oct 2026

R'enyi Differential Privacy Mechanisms for Posterior Sampling

arXiv 1710.00892 ↗First posted 2 Oct 2017cs.LG cs.AI cs.CRcited by 16

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Abstract (arXiv)

Using a recently proposed privacy definition of R'enyi Differential Privacy (RDP), we re-examine the inherent privacy of releasing a single sample from a posterior distribution. We exploit the impact of the prior distribution in mitigating the influence of individual data points. In particular, we focus on sampling from an exponential family and specific generalized linear models, such as logistic regression. We propose novel RDP mechanisms as well as offering a new RDP analysis for an existing method in order to add value to the RDP framework. Each method is capable of achieving arbitrary RDP privacy guarantees, and we offer experimental results of their efficacy.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Oct 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 2; 2024: 2; 2025: 1; 2026: 4.

16 papers.

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  1. 2022Submix: Practical Private Prediction for Large-Scale Language Modelscited by 31
  2. 2024Incentives in Private Collaborative Machine Learningcited by 10
  3. 2022The Saddle-Point Accountant for Differential Privacycited by 5
  4. 2022Learning-Augmented Private Algorithms for Multiple Quantile Releasecited by 5
  5. 2021Privacy Amplification Via Bernoulli Samplingcited by 4
  6. 2023Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacycited by 4
  7. 2021Dirichlet Mechanism for Differentially Private KL Divergence Minimizationcited by 2
  8. 2021Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning?cited by 2
  9. 2022Differentially private partitioned variational inferencecited by 2
  10. 2026Differential Privacy for Transformer Embeddings of Text with Nonparametric Variational Information Bottleneckcited by 2
  11. 2023Differentially Private Statistical Inference through β-Divergence One Posterior Samplingcited by 1
  12. 2024Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergencecited by 1
4 more
  1. 2026Computation-Utility-Privacy Tradeoffs in Bayesian Estimationcited by 1
  2. 2025Differential privacy guarantees of Markov chain Monte Carlo algorithmscited by 0
  3. 2026Differential Privacy of Gaussian Process Posterior Samplingcited by 0
  4. 2026On Inherent Privacy of Posterior Sampling: A Unified R'enyi-Divergence Frameworkcited by 0