| Summary: | Machine learning in sensitive domains like healthcare currently faces a major bottleneck due to the scarcity of data that is publicly available. Privacy protection regulations such as HIPAA and GDPR and recent progress in information estimation literature motivate us to investigate the issue from an information theoretic perspective. In this thesis, we propose InfoShape, an encoder training scheme that aims to maintain privacy while also preserving utility for downstream prediction tasks. We achieve this by utilizing mutual information neural estimation (MINE) [2] to estimate two quantities, privacy leakage: the mutual information between the original inputs and the encoded representations, and utility score: the mutual information between the encoded representations and the intended labeling information for classification. We train a neural network as our encoder by using our privacy and utility measures in a Lagrangian optimization. We show empirically on Gaussian generated data that InfoShape is capable of altering encoded sample outputs such that the privacy leakage is reduced and the utility score increases. Moreover, we observe that the classification accuracy of downstream models has a meaningful connection with the utility score, which improves after we train an encoder compared to the untrained encoder. This work has profound implications for privacy-preserving machine learning and could serve as a pivotal tool in the future for revolutionizing AI in areas like healthcare.
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