Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent
van de L'Isle, N.
van Egmond, M.B.
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
To reference this document use:
Privacy-preserving LASSO regression
Secure multi-party computation
Secure set intersection
Privacy by design
Gradient descent algorithms
Springer Science and Business Media Deutschland GmbH
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