# 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.

## Key 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.