2018/01/30 by Yi Li, Yitao Duan, Li, Yi +7
Computer Science · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.1801.10117
openalex publication_date 2018/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce PrivPy, a practical privacy-preserving collaborative computation framework, especially optimized for machine learning tasks. PrivPy provides an easy-to-use and highly compatible Python programming front-end which supports high-level array operations and different secure computation engines to allow for security assumptions and performance trade-offs. With PrivPy, programmers can write modern machine learning algorithms conveniently and efficiently in Python. We also design and implement a new efficient computation engine, with which people can use competing cloud providers to efficiently perform general arithmetics over real numbers. We demonstrate the usability and scalability of PrivPy using common machine learning models (e.g. logistic regression and convolutional neural networks) and real-world datasets (including a 5000-by-1-million matrix).