Optimized and federated soft-impute for privacy-preserving tensor completion in cyber-physical-social systems
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The proposed privacy-preserving tensor completion method can provide a privacy guarantee with high accuracy.
“Through a formal recovery error bound, the proposed privacy-preserving tensor completion method is proven that can provide a privacy guarantee with high accuracy.”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [48]
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- Authors
- Yang, J., Fu, C., & Lu, H.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Fu, Cai
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 564
- Pages
- 103-123
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W3131595479
- Cited by
- 14
- References
- 54
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 1
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Sciences (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Mathematics › Computational Mathematics › Tensor decomposition and applications
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- federated learning, differential privacy, cyber-physical-social systems, missing data recovery, privacy-utility trade-off, distributed data
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