Print Email Facebook Twitter Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent Title Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent Author Veugen, P.J.M. Kamphorst, B van de L'Isle, N. van Egmond, M.B. Publication year 2021 Abstract We show how multiple data-owning parties can collabora tively train several machine learning algorithms without jeopardizing the privacy of their sensitive data. In particular, we assume that every party knows specific features of an overlapping set of people. Using a secure implementation of an advanced hidden set intersection protocol and a privacy-preserving Gradient Descent algorithm, we are able to train a Ridge, LASSO or SVM model over the intersection of people in their data sets. Both the hidden set intersection protocol and privacy preserving LAS Subject Gradient descentPrivacy-preserving LASSO regressionSecure multi-party computationSecure set intersectionCryptographyDatabase systemsGradient methodsMachine learningPrivacy by designRegression analysisGradient descentGradient descent algorithmsMultiple dataPartitioned databasePrivacy preservingSecure implementationSensitive datasSet intersectionLearning algorithms To reference this document use: http://resolver.tudelft.nl/uuid:194629e0-7643-4f38-8635-94815762d687 TNO identifier 958263 Publisher Springer Science and Business Media Deutschland GmbH ISBN 9783030780852 ISSN 0302-9743 Source Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021, 8 July 2021 through 9 July 2021, 38-51 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.