The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning
Abstract
To improve the efficiency of big data feature learning, the paper proposes a privacy preserving deep computation model by offloading the expensive operations to the cloud. Privacy concerns become evident because there are a large number of private data by various applications in the smart city, such as sensitive data of governments or proprietary information of enterprises. To protect the private data, the proposed model uses the BGV encryption scheme to encrypt the private data and employs cloud servers to perform the high-order back-propagation algorithm on the encrypted data efficiently for deep computation model training. Furthermore, the proposed scheme approximates the Sigmoid function as a polynomial function to support the secure computation of the activation function with the BGV encryption. In our scheme, only the encryption operations and the decryption operations are performed by the client while all the computation tasks are performed on the cloud. Experimental results show that our scheme is improved by approximately 2.5 times in the training efficiency compared to the conventional deep computation model without disclosing the private data using the cloud computing including ten nodes. More importantly, our scheme is highly scalable by employing more cloud servers, which is particularly suitable for big data.
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Cites
A method for obtaining digital signatures and public-key cryptosystems
Rivest, R. L., Shamir, A., & Adleman, L. (1978) · Communications of the ACM · doi:10.1145/359340.359342 · Cited by 13,462
A method for obtaining digital signatures and public-key cryptosystems
Rivest, R. L., Shamir, A., & Adleman, L. (1983) · Communications of the ACM · doi:10.1145/357980.358017 · Cited by 13,012
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
Paillier, P. (2007) · Lecture Notes in Computer Science · doi:10.1007/3-540-48910-x_16 · Cited by 7,250
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
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335438 · Cited by 2,961
An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A. Y., & Lee, H. (2011) · Cited by 2,758
Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.109 · Cited by 2,410
(1978) · Cited by 1,875
(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
Cited by
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
A survey on deep learning for big data
Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.006 · Cited by 1,226
Self-citationDeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2952332 · Cited by 610
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · arXiv · doi:10.48550/arxiv.1902.04885 · Cited by 600
Gai, K., Qiu, M., & Sun, X. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.10.011 · Cited by 566
Metadata
- Authors
- Zhang, Q., Yang, L. T., & Chen, Z.
- Venue
- IEEE Transactions on Computers
- Published
- 2016-05-01
- Volume
- 65
- Issue
- 5
- Pages
- 1351-1362
- ISSN
- 0018-9340 (ISSN-L)
- OpenAlex
- W2317339301
- Cited by
- 286
- References
- 51
- FWCI
- 24.09
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
