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arXiv · 2505.05843

Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning

Linda Scheu-Hachtel·Jasmin Zalonis
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Abstract

Functional encryption (FE) has recently attracted interest in privacy-preserving machine learning (PPML) for its unique ability to compute specific functions on encrypted data. A related line of work focuses on noisy FE, which ensures differential privacy in the output while keeping the data encrypted. We extend the notion of noisy multi-input functional encryption (NMIFE) to (dynamic) noisy multi-client functional encryption ((Dy)NMCFE), which allows for more flexibility in the number of data holders and analyses, while protecting the privacy of the data holder with fine-grained access through the usage of labels. Following our new definition of DyNMCFE, we present DyNo, a concrete inner-product DyNMCFE scheme. Our scheme captures all the functionalities previously introduced in noisy FE schemes, while being significantly more efficient in terms of space and runtime and fulfilling a stronger security notion by allowing the corruption of clients. To further prove the applicability of DyNMCFE, we present a protocol for PPML based on DyNo. According to this protocol, we train a privacy-preserving logistic regression.

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Linda Scheu-Hachtel, Jasmin Zalonis. 2025-05-09. Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning. https://arxiv.org/abs/2505.05843

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