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CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning

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 نشر من قبل Jinhyun So
 تاريخ النشر 2019
  مجال البحث الهندسة المعلوماتية
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How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers. We characterize CodedPrivateMLs privacy threshold and prove its convergence for logistic (and linear) regression. Furthermore, via extensive experiments on Amazon EC2, we demonstrate that CodedPrivateML provides significant speedup over cryptographic approaches based on multi-party computing (MPC).



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