Securing Databases from Probabilistic Inference

TL;DR

This paper introduces Angerona, a probabilistic inference control mechanism based on ProbLog, which effectively prevents information leakage in databases with probabilistic dependencies, and demonstrates its scalability and practical relevance.

Contribution

It develops a novel foundation for database inference control using ProbLog and provides a tractable algorithm for a key fragment, enhancing security in probabilistic databases.

Findings

Angerona effectively prevents probabilistic information leakage.

The inference algorithm scales to security-critical problems.

Empirical evaluation shows practical performance of Angerona.

Abstract

Databases can leak confidential information when users combine query results with probabilistic data dependencies and prior knowledge. Current research offers mechanisms that either handle a limited class of dependencies or lack tractable enforcement algorithms. We propose a foundation for Database Inference Control based on ProbLog, a probabilistic logic programming language. We leverage this foundation to develop Angerona, a provably secure enforcement mechanism that prevents information leakage in the presence of probabilistic dependencies. We then provide a tractable inference algorithm for a practically relevant fragment of ProbLog. We empirically evaluate Angerona's performance showing that it scales to relevant security-critical problems.