DocumentCode :
2557146
Title :
Privacy-Preserving DBSCAN Clustering Over Vertically Partitioned Data
Author :
Wei-jiang, Xu ; Liu-Sheng, Huang ; Yong-long, Luo ; Yi-fei, Yao ; Wei-wei, Jing
Author_Institution :
Univ. of Sci. & Technol. of China, Beijing
fYear :
2007
fDate :
26-28 April 2007
Firstpage :
850
Lastpage :
856
Abstract :
Data mining has been a popular research area for more than a decade because of its ability of efficiently extracting statistics and trends from large sets of data. However, in many applications, the data are originally collected at different sites owned by different users. The distributed data mining raises concerns about the privacy of individuals. This paper considers the problem of privacy preserving DBSCAN clustering over vertically partitioned data based on some results of SMC. Each site learns the final results about the clusters, but learns nothing about any other site ´s data. An efficient secure intersection protocol is first proposed to implement privacy preserving DBSCAN clustering. The security and complexity of the protocols are also analyzed. The results show that the protocols preserve the privacy of the data and the time complexity as well as the communication complexity is acceptable.
Keywords :
communication complexity; data mining; data privacy; protocols; SMC; communication complexity; data mining; data privacy; privacy-preserving DBSCAN clustering; secure intersection protocol; secure multiparty computation; vertically partitioned data; Clustering algorithms; Cryptographic protocols; Cryptography; Data mining; Data privacy; Data security; Diseases; Partitioning algorithms; Protection; Sliding mode control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Ubiquitous Engineering, 2007. MUE '07. International Conference on
Conference_Location :
Seoul
Print_ISBN :
0-7695-2777-9
Type :
conf
DOI :
10.1109/MUE.2007.174
Filename :
4197380
Link To Document :
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