Reliability Check via Weight Similarity in Privacy-Preserving Multi-Party Machine Learning

TL;DR

This paper introduces a privacy-preserving multi-party machine learning scheme that uses a novel weight similarity metric to assess participant data quality while safeguarding data and model privacy through homomorphic encryption.

Contribution

It presents a new weight similarity metric and a secure scheme for data quality checking in multi-party learning, enhancing privacy and reliability.

Findings

The scheme accurately detects reliable participants.

It guarantees data and model privacy using homomorphic encryption.

Experimental results confirm effectiveness and privacy preservation.

Abstract

Multi-party machine learning is a paradigm in which multiple participants collaboratively train a machine learning model to achieve a common learning objective without sharing their privately owned data. The paradigm has recently received a lot of attention from the research community aimed at addressing its associated privacy concerns. In this work, we focus on addressing the concerns of data privacy, model privacy, and data quality associated with privacy-preserving multi-party machine learning, i.e., we present a scheme for privacy-preserving collaborative learning that checks the participants' data quality while guaranteeing data and model privacy. In particular, we propose a novel metric called weight similarity that is securely computed and used to check whether a participant can be categorized as a reliable participant (holds good quality data) or not. The problems of model and data privacy are tackled by integrating homomorphic encryption in our scheme and uploading encrypted weights, which prevent leakages to the server and malicious participants, respectively. The analytical and experimental evaluations of our scheme demonstrate that it is accurate and ensures data and model privacy.