The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Multi-key privacy-preserving deep learning in cloud computing
Open accessAbstract
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. In the basic scheme, we use M-FHE as our privacy-preserving technique. Only the decrypt operation needs the interaction among data owners.In the advanced scheme, we propose a hybrid structure scheme by combining the double decryption mechanism and FHE.In the advanced scheme, only the encrypt and decrypt algorithms are performed by data providers.We prove that these two multi-key privacy-preserving deep learning schemes over encrypted data are secure.
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Cites
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Fully homomorphic encryption using ideal lattices
Gentry, C. (2009) · Proceedings of the forty-first annual ACM symposium on Theory of computing · doi:10.1145/1536414.1536440 · Cited by 6,806
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335438 · Cited by 2,961
Goldwasser, S., & Micali, S. (1984) · Journal of Computer and System Sciences · doi:10.1016/0022-0000(84)90070-9 · Cited by 2,603
Privacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356
(Leveled) fully homomorphic encryption without bootstrapping
Brakerski, Z., Gentry, C., & Vaikuntanathan, V. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090262 · Cited by 1,782
Evaluating 2-DNF Formulas on Ciphertexts
Boneh, D., Goh, E.-J., & Nissim, K. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30576-7_18 · Cited by 1,692
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Metadata
- Authors
- Li, P., Li, J., Huang, Z., Li, T., Gao, C.-Z., Yiu, S.-M., & Chen, K.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 74
- Pages
- 76-85
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2597998853
- Cited by
- 462
- References
- 42
- FWCI
- 39.69
- Type
- journal article
- Language
- en
- Open access
- green
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- Li, P., Li, J., Huang, Z., Li, T., Gao, C.-Z., Yiu, S.-M., & Chen, K. (2017). Multi-key privacy-preserving deep learning in cloud computing. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.02.006
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