Next Article in Journal
How to Measure a Two-Sided Market
Next Article in Special Issue
Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation
Previous Article in Journal
Protecting Physical Layer Secret Key Generation from Active Attacks
Previous Article in Special Issue
Variational Message Passing and Local Constraint Manipulation in Factor Graphs

ABCDP: Approximate Bayesian Computation with Differential Privacy

Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany
Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany
Google Research, 80636 Munich, Germany
Author to whom correspondence should be addressed.
Academic Editor: Pierre Alquier
Entropy 2021, 23(8), 961;
Received: 19 May 2021 / Revised: 15 July 2021 / Accepted: 20 July 2021 / Published: 27 July 2021
(This article belongs to the Special Issue Approximate Bayesian Inference)
We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the sparse vector technique (SVT), widely studied in the differential privacy literature. SVT incurs the privacy cost only when a condition (whether a quantity of interest is above/below a threshold) is met. If the condition is sparsely met during the repeated queries, SVT can drastically reduce the cumulative privacy loss, unlike the usual case where every query incurs the privacy loss. In ABC, the quantity of interest is the distance between observed and simulated data, and only when the distance is below a threshold can we take the corresponding prior sample as a posterior sample. Hence, applying SVT to ABC is an organic way to transform an ABC algorithm to a privacy-preserving variant with minimal modification, but yields the posterior samples with a high privacy level. We theoretically analyzed the interplay between the noise added for privacy and the accuracy of the posterior samples. We apply ABCDP to several data simulators and show the efficacy of the proposed framework. View Full-Text
Keywords: approximate Bayesian computation (ABC); differential privacy (DP); sparse vector technique (SVT) approximate Bayesian computation (ABC); differential privacy (DP); sparse vector technique (SVT)
Show Figures

Figure 1

MDPI and ACS Style

Park, M.; Vinaroz, M.; Jitkrittum, W. ABCDP: Approximate Bayesian Computation with Differential Privacy. Entropy 2021, 23, 961.

AMA Style

Park M, Vinaroz M, Jitkrittum W. ABCDP: Approximate Bayesian Computation with Differential Privacy. Entropy. 2021; 23(8):961.

Chicago/Turabian Style

Park, Mijung, Margarita Vinaroz, and Wittawat Jitkrittum. 2021. "ABCDP: Approximate Bayesian Computation with Differential Privacy" Entropy 23, no. 8: 961.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop