The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
An Efficient Privacy-Preserving Outsourced Calculation Toolkit With Multiple Keys
Open accessAbstract
In this paper, we propose a toolkit for efficient and privacy-preserving outsourced calculation under multiple encrypted keys (EPOM). Using EPOM, a large scale of users can securely outsource their data to a cloud server for storage. Moreover, encrypted data belonging to multiple users can be processed without compromising on the security of the individual user's (original) data and the final computed results. To reduce the associated key management cost and private key exposure risk in EPOM, we present a distributed two-trapdoor public-key cryptosystem, the core cryptographic primitive. We also present the toolkit to ensure that the commonly used integer operations can be securely handled across different encrypted domains. We then prove that the proposed EPOM achieves the goal of secure integer number processing without resulting in privacy leakage of data to unauthorized parties. Last, we demonstrate the utility and the efficiency of EPOM using simulations.
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A method for obtaining digital signatures and public-key cryptosystems
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Cited by
ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning
Ma, Z., Ma, J., Miao, Y., Li, Y., & Deng, R. H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3169918 · Cited by 371
Self-citationPrivacy and Security Issues in Deep Learning: A Survey
Liu, X., Xie, L., Wang, Y., Zou, J., Xiong, J., Ying, Z., & Vasilakos, A. V. (2021) · IEEE Access · doi:10.1109/access.2020.3045078 · Cited by 282
Self-citationPrivacy-Preserving Federated Learning Using Homomorphic Encryption
Park, J., & Lim, H. (2022) · Applied Sciences · doi:10.3390/app12020734 · Cited by 223
Achieving Searchable and Privacy-Preserving Data Sharing for Cloud-Assisted E-Healthcare System
Xu, C., Wang, N., Zhu, L., Sharif, K., & Zhang, C. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2917186 · Cited by 105
A privacy-preserving sensory data sharing scheme in Internet of Vehicles
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Metadata
- Authors
- Liu, X., Deng, R. H., Choo, K.-K. R., & Weng, J.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2016-11
- Volume
- 11
- Issue
- 11
- Pages
- 2401-2414
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2405356014
- Cited by
- 264
- References
- 49
- FWCI
- 27.39
- Type
- journal article
- Language
- en
- Open access
- green
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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.
