Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent

Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent

Veugen, P.J.M.
Kamphorst, B
van de L'Isle, N.
van Egmond, M.B.

2021

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

Gradient descent
Privacy-preserving LASSO regression
Secure multi-party computation
Secure set intersection
Cryptography
Database systems
Gradient methods
Machine learning
Privacy by design
Regression analysis
Gradient descent
Gradient descent algorithms
Multiple data
Partitioned database
Privacy preserving
Secure implementation
Sensitive datas
Set intersection
Learning algorithms

http://resolver.tudelft.nl/uuid:194629e0-7643-4f38-8635-94815762d687

958263

Springer Science and Business Media Deutschland GmbH

9783030780852

0302-9743

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

conference paper