TY - JOUR
T1 - PPMCK
T2 - Privacy-preserving multi-party computing for K-means clustering
AU - Fan, Yongkai
AU - Bai, Jianrong
AU - Lei, Xia
AU - Lin, Weiguo
AU - Hu, Qian
AU - Wu, Guodong
AU - Guo, Jiaming
AU - Tan, Gang
N1 - Funding Information:
This work was partially supported by the National Key R&D Program of China (No. 2018YFB0803700 ), CERNET Innovation Project ( NGII20180406 ), the Teaching Innovation Project of Communication University of China ( JG2002 ), Fundamental Research Funds for the Central Universities , and by the National Natural Science Foundation of China under grant No. 62072170 .
Funding Information:
Gang Tan received his B.E. in Computer Science from Tsinghua University in 1999, and his Ph.D. in Computer Science from Princeton University in 2005. He is an Associate Professor in Penn State University, University Park, USA. He was a recipient of an NSF Career award and won James F. Will Career Development Professorship. He leads the Security of Software (SOS) lab at Penn State. He is interested in methodologies that help create reliable and secure software systems.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/8
Y1 - 2021/8
N2 - The powerful resource advantage of the cloud provides a suitable computing environment for data processing. By transferring local computing to the cloud, the efficiency of data processing can be improved. However, the open cloud environment has defects in data privacy-preserving. In order to strengthen the protection of data privacy and ensure the security of multi-party interaction, we propose a privacy-preserving multi-party computing scheme for K-means clustering (PPMCK). PPMCK can preserve data privacy in the cloud and in the local side for each party from multi-party computing. In addition, PPMCK uses homomorphic encryption to protect data privacy. To support the division operation and ciphertext value size comparison with which homomorphic encryption cannot handle, the corresponding measurements are adopted, which make homomorphic encryption work smoothly. The experimental results demonstrate that PPMCK is effective in both data processing and privacy-preserving.
AB - The powerful resource advantage of the cloud provides a suitable computing environment for data processing. By transferring local computing to the cloud, the efficiency of data processing can be improved. However, the open cloud environment has defects in data privacy-preserving. In order to strengthen the protection of data privacy and ensure the security of multi-party interaction, we propose a privacy-preserving multi-party computing scheme for K-means clustering (PPMCK). PPMCK can preserve data privacy in the cloud and in the local side for each party from multi-party computing. In addition, PPMCK uses homomorphic encryption to protect data privacy. To support the division operation and ciphertext value size comparison with which homomorphic encryption cannot handle, the corresponding measurements are adopted, which make homomorphic encryption work smoothly. The experimental results demonstrate that PPMCK is effective in both data processing and privacy-preserving.
UR - http://www.scopus.com/inward/record.url?scp=85105698861&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105698861&partnerID=8YFLogxK
U2 - 10.1016/j.jpdc.2021.03.009
DO - 10.1016/j.jpdc.2021.03.009
M3 - Article
AN - SCOPUS:85105698861
SN - 0743-7315
VL - 154
SP - 54
EP - 63
JO - Journal of Parallel and Distributed Computing
JF - Journal of Parallel and Distributed Computing
ER -