QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems

TL;DR

QuerySnout is an automated tool that discovers attribute inference vulnerabilities in query-based systems by analyzing their responses, enabling robust privacy evaluation without manual attack development.

Contribution

This paper introduces QuerySnout, the first automated method for discovering attribute inference attacks against query-based systems, improving efficiency and scope of privacy vulnerability assessment.

Findings

QS outperforms existing attacks in effectiveness.

It can analyze various datasets and protection mechanisms.

QS can be extended to systems with privacy budgets.

Abstract

Although query-based systems (QBS) have become one of the main solutions to share data anonymously, building QBSes that robustly protect the privacy of individuals contributing to the dataset is a hard problem. Theoretical solutions relying on differential privacy guarantees are difficult to implement correctly with reasonable accuracy, while ad-hoc solutions might contain unknown vulnerabilities. Evaluating the privacy provided by QBSes must thus be done by evaluating the accuracy of a wide range of privacy attacks. However, existing attacks require time and expertise to develop, need to be manually tailored to the specific systems attacked, and are limited in scope. In this paper, we develop QuerySnout (QS), the first method to automatically discover vulnerabilities in QBSes. QS takes as input a target record and the QBS as a black box, analyzes its behavior on one or more datasets, and outputs a multiset of queries together with a rule to combine answers to them in order to reveal the sensitive attribute of the target record. QS uses evolutionary search techniques based on a novel mutation operator to find a multiset of queries susceptible to lead to an attack, and a machine learning classifier to infer the sensitive attribute from answers to the queries selected. We showcase the versatility of QS by applying it to two attack scenarios, three real-world datasets, and a variety of protection mechanisms. We show the attacks found by QS to consistently equate or outperform, sometimes by a large margin, the best attacks from the literature. We finally show how QS can be extended to QBSes that require a budget, and apply QS to a simple QBS based on the Laplace mechanism. Taken together, our results show how powerful and accurate attacks against QBSes can already be found by an automated system, allowing for highly complex QBSes to be automatically tested "at the pressing of a button".