ABCDP: Approximate Bayesian Computation with Differential Privacy

TL;DR

This paper introduces ABCDP, a new approximate Bayesian computation method that leverages differential privacy via the Sparse Vector Technique to produce privacy-preserving posterior samples with minimal accuracy loss.

Contribution

It presents a novel ABC framework integrating SVT for differential privacy, reducing privacy loss during sparse query conditions, with theoretical analysis of privacy-accuracy trade-offs.

Findings

Achieves high privacy levels with minimal modification to standard ABC.

Reduces cumulative privacy loss by applying SVT in ABC.

Provides theoretical bounds on privacy and accuracy trade-offs.

Abstract

We develop a novel approximate Bayesian computation (ABC) framework, ABCDP, that 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 met sparsely during the repeated queries, SVT can drastically reduces 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, 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 analyze the interplay between the noise added for privacy and the accuracy of the posterior samples.