Summary of Information Leakage Detection Through Approximate Bayes-optimal Prediction, by Pritha Gupta et al.
Information Leakage Detection through Approximate Bayes-optimal Prediction
by Pritha Gupta, Marcel Wever, Eyke Hüllermeier
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework for detecting information leakage (IL) in publicly available data. The authors address the limitations of conventional statistical approaches, which struggle with dimensionality issues, convergence, computational complexity, and misestimation of mutual information (MI). Instead, they develop a theoretical framework combining statistical learning theory and information theory to quantify and detect IL accurately. Using automated machine learning, they show that MI can be estimated by approximating the Bayes predictor’s log-loss and accuracy. The method outperforms state-of-the-art baselines in an empirical study using synthetic and real-world OpenSSL TLS server datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Information leakage is a major concern in today’s data-driven world. Imagine sharing sensitive information without realizing it! This paper helps solve this problem by developing a new way to detect when important info gets leaked. They improve on old methods that struggle with too much data, slow processing, and bad estimates. Instead, they use machine learning and statistical ideas to create a better framework. This method works well in tests using real-world examples. |
Keywords
* Artificial intelligence * Machine learning
