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Published Articles >> Table of Contents >> Abstract
2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07)
pp. 850-856
Privacy-Preserving DBSCAN Clustering Over Vertically Partitioned Data
XU Wei-jiang, University of Science and Technology of China
HUANG Liu-sheng, University of Science and Technology of China
LUO Yong-long, University of Science and Technology of China
YAO Yi-fei, University of Science and Technology of China
JING Wei-wei, University of Science and Technology of China
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MUE.2007.174
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| Abstract |
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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.
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 Additional Information
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Citation:
XU Wei-jiang, HUANG Liu-sheng, LUO Yong-long, YAO Yi-fei, JING Wei-wei,
"Privacy-Preserving DBSCAN Clustering Over Vertically Partitioned Data,"
mue,
pp. 850-856,
2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07),
2007
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