Information Leakage Detection through Approximate Bayes-optimal Prediction

2024-01-25 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Data Science & Analytics · Depth: Expert, quick

Summary

A new theoretical framework, developed by Pritha Gupta, Marcel Wever, and Eyke Hüllermeier, addresses the information leakage (IL) problem, where sensitive data is unintentionally exposed through observable system information. This framework quantifies and detects IL using statistical learning theory and information theory, overcoming limitations of conventional mutual information (MI) estimation, which struggles with dimensionality and computational complexity. It also expands beyond supervised machine learning methods restricted to binary sensitive information. The approach accurately estimates MI by approximating the Bayes predictor's log-loss and accuracy through automated machine learning. Empirical studies on synthetic and real-world OpenSSL TLS server datasets demonstrate its superior performance compared to existing baselines.

Key takeaway

For AI Security Engineers tasked with identifying subtle data exposures, this framework offers a robust alternative to traditional mutual information methods, which often struggle under high dimensionality and misestimation. You should consider integrating this Bayes-optimal prediction approach to more accurately quantify information leakage, especially in complex systems like OpenSSL TLS servers. This method can enhance your detection capabilities, provide a comprehensive framework beyond binary sensitive information, and potentially reduce false positives in critical security assessments.

Key insights

A new framework detects information leakage by approximating Bayes-optimal prediction to estimate mutual information.

Principles

• Mutual information can be estimated via Bayes predictor's log-loss.
• Statistical learning theory quantifies information leakage.

Method

Estimate mutual information by approximating the Bayes predictor's log-loss and accuracy using automated machine learning techniques.

In practice

• Detect information leakage in TLS server datasets.
• Quantify sensitive data exposure in data-driven systems.

Topics

• Information Leakage Detection
• Mutual Information Estimation
• Bayes-optimal Prediction
• Statistical Learning Theory
• Automated Machine Learning
• TLS Security

Best for: Research Scientist, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.