Journal
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume 74, Issue -, Pages 76-85Publisher
ELSEVIER
DOI: 10.1016/j.future.2017.02.006
Keywords
Cryptography; Machine learning; Fully homomorphic encryption; Cloud computing
Categories
Funding
- National Natural Science Foundation of China [61472091]
- Natural Science Foundation of Guangdong Province for Distinguished Young Scholars [2014A030306020]
- Science and Technology Planning Project of Guangdong Province, China [2015B010129015]
- Innovation Team Project of Guangdong Universities [2015KCXTD014]
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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. (C) 2017 Elsevier B.V. All rights reserved.
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