%0 Generic %1 li2018privpy %A Li, Yi %A Duan, Yitao %A Yu, Yu %A Zhao, Shuoyao %A Xu, Wei %D 2018 %K PrivPy Privacy Security deepscan %T PrivPy: Enabling Scalable and General Privacy-Preserving Machine Learning %U http://arxiv.org/abs/1801.10117 %X 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).

@misc{li2018privpy, abstract = {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).}, added-at = {2019-05-15T14:51:38.000+0200}, author = {Li, Yi and Duan, Yitao and Yu, Yu and Zhao, Shuoyao and Xu, Wei}, biburl = {https://www.bibsonomy.org/bibtex/286a72aa424a2967d731ec57a8a2b82ca/mittim}, description = {PrivPy: Enabling Scalable and General Privacy-Preserving Machine Learning}, interhash = {443f9e2989d5056ca4fc109f38e32579}, intrahash = {86a72aa424a2967d731ec57a8a2b82ca}, keywords = {PrivPy Privacy Security deepscan}, note = {cite arxiv:1801.10117}, timestamp = {2019-05-15T14:51:38.000+0200}, title = {PrivPy: Enabling Scalable and General Privacy-Preserving Machine Learning}, url = {http://arxiv.org/abs/1801.10117}, year = 2018 }