Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent
conference paper
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.
Topics
Privacy-preserving LASSO regressionSecure multi-party computationSecure set intersectionCryptographyDatabase systemsGradient methodsMachine learningPrivacy by designRegression analysisGradient descent algorithmsMultiple dataPartitioned databaseSecure implementationSensitive datasSet intersectionLearning algorithms
TNO Identifier
958263
ISSN
03029743
ISBN
9783030780852
Publisher
Springer
Source title
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, 8-9 July 2021
Collation
14 p.
Place of publication
Heidelberg, Germany
Pages
38-51
Files
To receive the publication files, please send an e-mail request to TNO Repository.