Efficient two-party privacy-preserving collaborative k-means clustering protocol supporting both storage and computation outsourcing-Reference-Cited by-同舟云学术

Efficient two-party privacy-preserving collaborative k-means clustering protocol supporting both storage and computation outsourcing

Published:2020-05 Issue: Volume:518 Page:168-180
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Jiang Zoe L.^ORCID,Guo Ning,Jin Yabin,Lv Jiazhuo,Wu Yulin,Liu Zechao,Fang Junbin^ORCID,Yiu S.M.,Wang Xuan

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference29 articles.

1. Privacy preserving data mining;Lindell,2000

2. Privacy-preserving data mining;Agrawal,2000

3. Privacy-preserving k-means clustering over vertically partitioned data;Vaidya,2003

4. Privacy preserving clustering;Jha,2005

5. Privacy-preserving distributed k-means clustering over arbitrarily partitioned data;Jagannathan,2005

Cited by 30 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 multi-party logistic regression in cloud computing;Computer Standards & Interfaces;2024-08

2. Efficient privacy-preserving outsourced k-means clustering on distributed data;Information Sciences;2024-07

3. How to Securely and Efficiently Solve the Large-Scale Modular System of Linear Equations on the Cloud;IEEE Transactions on Cloud Computing;2024-07

4. Differentially Private K-Means Publishing with Distributed Dimensions;2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD);2024-05-08

5. A nondominated sorting genetic model for co-clustering;Information Sciences;2024-05