The Differential Privacy of Bayesian Inference

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The Value of Collaboration in Convex Machine Learning with Differential Privacy (2019, background): “A natural way for alleviating privacy concerns is to deploy privacy-enabled ML using differential privacy (DP) [16]–[19].”
Differentially Private Bayesian Linear Regression (2019, methodology): “…Zhang et al., 2016], posterior sampling (OPS) [Dimitrakakis et al., 2014, Geumlek et al., 2017, Minami et al., 2016, Wang, 2018, Wang et al., 2015, Zhang et al., 2016], subsample and aggregate [Dwork and Smith, 2010, Smith, 2008], objective perturbation [Kifer et al., 2012], and noisy stochastic…”
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis (2016, methodology): “Our approach is equivalent to a generalization of Zhang et al. (2016)’s recently and independently proposed algo-rithm for beta-Bernoulli systems.”
Renyi Differential Privacy Mechanisms for Posterior Sampling (2017, background): “[10, 20] provide a simple statistically efficient algorithm for sampling from exponential family posteriors.”
Differentially Private Bayesian Inference for Exponential Families (2018, methodology): “The ﬁrst method uses the same release mechanism as our Gibbs sampler and performs conjugate updates using the noisy sufﬁcient statistics [12, 13].”