Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning

TL;DR

This paper introduces a new dynamic noisy multi-client functional encryption scheme that enhances privacy, efficiency, and flexibility for privacy-preserving machine learning, enabling secure multi-party logistic regression training.

Contribution

It extends noisy functional encryption to a dynamic multi-client setting, proposes a concrete efficient scheme, and demonstrates its application in privacy-preserving logistic regression.

Findings

The DyNo scheme is more space- and time-efficient than previous schemes.

DyNo supports client corruption, strengthening security.

The protocol successfully trains a privacy-preserving logistic regression model.

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.