Statistic selection and MCMC for differentially private Bayesian estimation

Alparslan, Barış and Yıldırım, Sinan (2022) Statistic selection and MCMC for differentially private Bayesian estimation. Statistics and Computing, 32 (5). ISSN 0960-3174 (Print) 1573-1375 (Online)

This is the latest version of this item.

Full text not available from this repository. (Request a copy)


This paper concerns differentially private Bayesian estimation of the parameters of a population distribution, when a noisy statistic of a sample from that population is shared to provide differential privacy. This work mainly addresses two problems. (1) What statistics of the sample should be shared privately? For this question, we promote using the Fisher information. We find out that the statistic that is most informative in a non-privacy setting may not be the optimal choice under the privacy restrictions. We provide several examples to support that point. We consider several types of data sharing settings and propose several Monte Carlo-based numerical estimation methods for calculating the Fisher information for those settings. The second question concerns inference: (2) Based on the shared statistics, how could we perform effective Bayesian inference? We propose several Markov chain Monte Carlo (MCMC) algorithms for sampling from the posterior distribution of the parameter given the noisy statistic. The proposed MCMC algorithms can be preferred over one another depending on the problem. For example, when the shared statistic is additive and added Gaussian noise, a simple Metropolis-Hasting algorithm that utilises the central limit theorem is a decent choice. We propose more advanced MCMC algorithms for several other cases of practical relevance. Our numerical examples involve comparing several candidate statistics to be shared privately. For each statistic, we perform Bayesian estimation based on the posterior distribution conditional on the privatised version of that statistic. We demonstrate that the relative performance of a statistic, in terms of the mean squared error of the Bayesian estimator based on the corresponding privatised statistic, is adequately predicted by the Fisher information of the privatised statistic.
Item Type: Article
Uncontrolled Keywords: Bayesian statistics; Differential privacy; Fisher information; Markov chain Monte Carlo; Statistic selection
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Sinan Yıldırım
Date Deposited: 02 Sep 2022 14:32
Last Modified: 02 Sep 2022 14:32

Available Versions of this Item

Actions (login required)

View Item
View Item