P3LS: Partial Least Squares under Privacy Preservation

TL;DR

This paper introduces P3LS, a privacy-preserving federated learning method for process modeling in manufacturing value chains, enabling cross-organizational data integration without compromising privacy.

Contribution

It presents a novel SVD-based PLS algorithm with privacy masks, demonstrating its effectiveness and privacy guarantees in a multi-party manufacturing scenario.

Findings

P3LS improves prediction accuracy of process KPIs.

P3LS is numerically equivalent to traditional PLS on simulated data.

The privacy analysis confirms strong data protection guarantees.

Abstract

Modern manufacturing value chains require intelligent orchestration of processes across company borders in order to maximize profits while fostering social and environmental sustainability. However, the implementation of integrated, systems-level approaches for data-informed decision-making along value chains is currently hampered by privacy concerns associated with cross-organizational data exchange and integration. We here propose Privacy-Preserving Partial Least Squares (P3LS) regression, a novel federated learning technique that enables cross-organizational data integration and process modeling with privacy guarantees. P3LS involves a singular value decomposition (SVD) based PLS algorithm and employs removable, random masks generated by a trusted authority in order to protect the privacy of the data contributed by each data holder. We demonstrate the capability of P3LS to vertically integrate process data along a hypothetical value chain consisting of three parties and to improve the prediction performance on several process-related key performance indicators. Furthermore, we show the numerical equivalence of P3LS and PLS model components on simulated data and provide a thorough privacy analysis of the former. Moreover, we propose a mechanism for determining the relevance of the contributed data to the problem being addressed, thus creating a basis for quantifying the contribution of participants.