More efficient oblivious transfer and extensions for faster secure computation
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Privacy-preserving matrix factorization
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
ACM SIGOPS 24th Symposium on Operating Systems Principles
Verifying computations with state
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
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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.