P3LS: Partial Least Squares under Privacy Preservation
- URL: http://arxiv.org/abs/2401.14884v1
- Date: Fri, 26 Jan 2024 14:08:43 GMT
- Title: P3LS: Partial Least Squares under Privacy Preservation
- Authors: Du Nguyen Duy, Ramin Nikzad-Langerodi
- Abstract summary: We propose Privacy-Preserving Partial Least Squares (P3LS) regression, a novel federated learning technique.
P3LS involves a singular value decomposition (SVD) based PLS algorithm and employs removable, random masks.
We demonstrate the capability of P3LS to vertically integrate process data along a hypothetical value chain consisting of three parties.
- Score: 1.14219428942199
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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