A Quantization-based Technique for Privacy Preserving Distributed Learning

TL;DR

This paper introduces a quantization-based privacy-preserving technique for distributed machine learning that protects data and model parameters using a hash-based protocol with demonstrated robustness and accuracy.

Contribution

It presents a novel, regulation-compliant method employing quantized hashing and randomization for privacy in distributed ML training, applicable across the entire ML lifecycle.

Findings

Robustness demonstrated in experiments

Maintains accuracy while preserving privacy

Applicable with standard secure computation protocols

Abstract

The massive deployment of Machine Learning (ML) models raises serious concerns about data protection. Privacy-enhancing technologies (PETs) offer a promising first step, but hard challenges persist in achieving confidentiality and differential privacy in distributed learning. In this paper, we describe a novel, regulation-compliant data protection technique for the distributed training of ML models, applicable throughout the ML life cycle regardless of the underlying ML architecture. Designed from the data owner's perspective, our method protects both training data and ML model parameters by employing a protocol based on a quantized multi-hash data representation Hash-Comb combined with randomization. The hyper-parameters of our scheme can be shared using standard Secure Multi-Party computation protocols. Our experimental results demonstrate the robustness and accuracy-preserving properties of our approach.