RT Journal Article
T1 SECURE MULTIPARTY COMPUTATION FOR PREDICTIVE MAINTENANCE: 												VALIDATION OF SCALE-MAMBA IN TERMS OF ACCURACY AND EFFICIENCY
A1 Gamiz-Ugarte, Idoia
A1 Lage-Serrano, Oscar
A1 Legarreta-Solaguren, Leire
A1 Regueiro-Senderos, Cristina
A1 Jacob-Taquet, Eduardo
A1 Seco-Aguirre, Iñaki
AB Privacy is a booming sector and there is an increasing number of limitations that hinder the centralization of data coming from different sources. Nowadays, having data provides value and an advantage over the rest, since it allows the performance of a wider and more generalizable analysis. Secure Multiparty Computation (SMPC) is a cryptographic technique that allows performing computations with data from different parties while maintaining the privacy of the data and avoiding centralization. This work focuses on the SCALE-MAMBA framework for conducting SMPC and the main objective is its validation in terms of types of operations, the accuracy of the results and execution times. A use case that is directly related to the industry is used, consisting of a manufacturer who wants to implement predictive maintenance on a machine whose data is collected by different users. Two types of scenarios are presented in order to analyze the results, obtaining different conclusions for each of them. On the one hand, the first scenario collects the use cases in which the aim is to compute statistics or simple calculations with data in common. On the other hand, the second scenario focuses on the training of Machine Learning (ML) algorithms. The original contribution of this work includes the implementation of these codes within the Mamba language, their application to concrete data, and the comparison of the results with those that would be obtained by performing it in an insecure way, centralizing the data, and using R or Python. The major limitations encountered are around execution times, which might be acceptable for many use cases in the first scenario, but are prohibitive for many of the techniques used in real ML training.
SN 0012-7361
YR 2022
FD 2022-11
LK https://hdl.handle.net/11556/3834
UL https://hdl.handle.net/11556/3834
LA eng
NO Gamiz-Ugarte , I , Lage-Serrano , O , Legarreta-Solaguren , L , Regueiro-Senderos , C , Jacob-Taquet , E & Seco-Aguirre , I 2022 , ' SECURE MULTIPARTY COMPUTATION FOR PREDICTIVE MAINTENANCE : VALIDATION OF SCALE-MAMBA IN TERMS OF ACCURACY AND EFFICIENCY ' , Dyna (Spain) , vol. 97 , no. 6 , pp. 613-619 . https://doi.org/10.6036/10579
NO Publisher Copyright: © 2022 Publicaciones Dyna Sl. All rights reserved.
NO This work was partially supported by the Department of Economic Development, Sustainability and Environment of the Basque Government under the project “Infraestructura federada de Experimentación para aplicaciones Industria 4.0, B-Ind5G” with reference KK-2021/00026, and by TECNALIA and the University of the Basque Country (UPV/EHU) under grant PIFTEC21/05.
DS TECNALIA Publications
RD 2 sept 2024