Securing Databases from Probabilistic Inference
This addresses database security for users handling sensitive data with probabilistic dependencies, representing an incremental improvement by building on existing probabilistic logic methods.
The paper tackles the problem of confidential information leakage in databases due to probabilistic inference from query results, proposing a secure enforcement mechanism called Angerona that scales to security-critical problems.
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.