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2013 IEEE Symposium on Security and Privacy (2013)
Berkeley, CA, USA USA
May 19, 2013 to May 22, 2013
ISSN: 1081-6011
ISBN: 978-1-4673-6166-8
pp: 334-348
Ridge regression is an algorithm that takes as input a large number of data points and finds the best-fit linear curve through these points. The algorithm is a building block for many machine-learning operations. We present a system for privacy-preserving ridge regression. The system outputs the best-fit curve in the clear, but exposes no other information about the input data. Our approach combines both homomorphic encryption and Yao garbled circuits, where each is used in a different part of the algorithm to obtain the best performance. We implement the complete system and experiment with it on real data-sets, and show that it significantly outperforms pure implementations based only on homomorphic encryption or Yao circuits.

V. Nikolaenko, U. Weinsberg, S. Ioannidis, M. Joye, D. Boneh and N. Taft, "Privacy-Preserving Ridge Regression on Hundreds of Millions of Records," 2013 IEEE Symposium on Security and Privacy(SP), Berkeley, CA, USA USA, 2013, pp. 334-348.
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