SECURE MULTIPARTY COMPUTATION FOR PREDICTIVE MAINTENANCE: VALIDATION OF SCALE-MAMBA IN TERMS OF ACCURACY AND EFFICIENCY

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dc.contributor.authorGamiz-Ugarte, Idoia
dc.contributor.authorLage-Serrano, Oscar
dc.contributor.authorLegarreta-Solaguren, Leire
dc.contributor.authorRegueiro-Senderos, Cristina
dc.contributor.authorJacob-Taquet, Eduardo
dc.contributor.authorSeco-Aguirre, Iñaki
dc.contributor.institutionCIBERSEC&DLT
dc.date.accessioned2024-07-24T12:08:06Z
dc.date.available2024-07-24T12:08:06Z
dc.date.issued2022-11
dc.descriptionPublisher Copyright: © 2022 Publicaciones Dyna Sl. All rights reserved.
dc.description.abstractPrivacy 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.en
dc.description.sponsorshipThis 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.
dc.description.statusPeer reviewed
dc.format.extent7
dc.identifier.citationGamiz-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
dc.identifier.doi10.6036/10579
dc.identifier.issn0012-7361
dc.identifier.urihttps://hdl.handle.net/11556/3834
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85146478962&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofDyna (Spain)
dc.relation.projectIDDepartment of Economic Development
dc.relation.projectIDTECNALIA
dc.relation.projectIDEusko Jaurlaritza, KK-2021/00026
dc.relation.projectIDEuskal Herriko Unibertsitatea, EHU, PIFTEC21/05
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsPrivacy-Preserving Computation
dc.subject.keywordsPrivacy-enhancing technologies
dc.subject.keywordsSCALE-MAMBA
dc.subject.keywordsSecure Multiparty Computation
dc.subject.keywordsaccuracy
dc.subject.keywordsclassification
dc.subject.keywordscryptography
dc.subject.keywordsdata analysis
dc.subject.keywordsefficiency
dc.subject.keywordsmachine learning
dc.subject.keywordsprediction
dc.subject.keywordspredictive maintenance
dc.subject.keywordsprivacy
dc.subject.keywordssecurity
dc.subject.keywordsGeneral Engineering
dc.titleSECURE MULTIPARTY COMPUTATION FOR PREDICTIVE MAINTENANCE: VALIDATION OF SCALE-MAMBA IN TERMS OF ACCURACY AND EFFICIENCYen
dc.typejournal article
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