Accepted Papers

PrivPy: General and Scalable Privacy-Preserving Data Mining

Yi Li (Tsinghua University);Wei Xu (Tsinghua University);


Privacy is a big hurdle for collaborative data mining across multiple parties. We present multi-party computation (MPC) framework designed for large-scale data mining tasks. PrivPy combines an easy-to-use and highly flexible Python programming interface with state-of-the-art secret-sharing-based MPC backend. With essential data types and operations (such as NumPy arrays and broadcasting), as well as automatic code-rewriting, programmers can write modern data mining algorithms conveniently in familiar Python. We demonstrate that we can support many real-world machine learning algorithms (e.g. logistic regression and convolutional neural networks) and large datasets (e.g. 5000-by-1-million matrix) with minimal algorithm porting effort.

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