Efficient Multiparty Fully Homomorphic Encryption With Computation Fairness and Error Detection in Privacy Preserving Multisource Data Mining-Reference-Cited by-同舟云学术

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Efficient Multiparty Fully Homomorphic Encryption With Computation Fairness and Error Detection in Privacy Preserving Multisource Data Mining

Published:2023-12 Issue:4 Volume:72 Page:1308-1323
ISSN:0018-9529
Container-title:IEEE Transactions on Reliability
language:
Short-container-title:IEEE Trans. Rel.

Author:

Guo Guanglai¹^ORCID,Zhu Yan¹^ORCID,Chen E.¹^ORCID,Yu Ruyun²,Zhang Lejun³^ORCID,Lv Kewei⁴,Feng Rongquan⁵

Affiliation:

1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

2. China Electronics Technology Research Institute of Cyberspace Security Company Ltd., Beijing, China

3. Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, China

4. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

5. School of Mathematical Sciences, Peking University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/24/10339143/10066160.pdf?arnumber=10066160

Reference41 articles.

1. Privacy preserving association rule mining in vertically partitioned data

2. General Secure Multi-party Computation from any Linear Secret-Sharing Scheme

3. Building decision tree classifier on private data;du;Proc IEEE Int Conf Privacy Secur Data Mining Australian Comput Soc Sydney Australia,0

4. Simplified VSS and fast-track multiparty computations with applications to threshold cryptography

5. Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Privacy-Preserving Queries Using Multisource Private Data Counting on Real Numbers in IoT;IEEE Internet of Things Journal;2023

2. Privacy-Preserving Collaborative Queries in Services Computing using Multisource Private Data Counting;IEEE Transactions on Services Computing;2023

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