Research Area: Machine Learning
Deep learning has attracted a lot of attention and has been applied successfully in many areas such as bioinformatics, imaging processing, game playing and computer security etc. On the other hand, deep learning usually requires a lot of training data which may not be provided by a sole owner. As the volume of data gets huge, it is common for users to store their data in a third-party cloud. Due to the confidentiality of the data, data are usually stored in encrypted form. To apply deep learning to these datasets owned by multiple data owners on cloud, we need to tackle two challenges: (i) the data are encrypted with different keys, all operations including intermediate results must be secure; and (ii) the computational cost and the communication cost of the data owner(s) should be kept minimal. In our work, we propose two schemes to solve the above problems. We first present a basic scheme based on multi-key fully homomorphic encryption (MK-FHE), then we propose an advanced scheme based on a hybrid structure by combining the double decryption mechanism and fully homomorphic encryption (FHE). We also prove that these two multi-key privacy-preserving deep learning schemes over encrypted data are secure.
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Author(s) Name: PingLi, JinLi, Zhengan Huang, TongLi, Chong-ZhiGao, Siu-Ming Yiu, Kai Chen
Journal name: Future Generation Computer Systems
Conferrence name:
Publisher name: ELSEVIER
DOI: https://doi.org/10.1016/j.future.2017.02.006
Volume Information: Volume 74, September 2017, Pages 76-85
