TY - GEN
T1 - Towards comprehensive privacy protection in data clustering
AU - Zhang, Nan
PY - 2007
Y1 - 2007
N2 - We address the protection of private information in data clustering. Previous work focuses on protecting the privacy of data being mined. We find that the cluster labels of individual data points can also be sensitive to data owners. Thus, we propose a privacy-preserving data clustering scheme that extracts accurate clustering rules from private data while protecting the privacy of both original data and individual cluster labels. We derive theoretical bounds on the performance of our scheme, and evaluate it experimentally with real-world data.
AB - We address the protection of private information in data clustering. Previous work focuses on protecting the privacy of data being mined. We find that the cluster labels of individual data points can also be sensitive to data owners. Thus, we propose a privacy-preserving data clustering scheme that extracts accurate clustering rules from private data while protecting the privacy of both original data and individual cluster labels. We derive theoretical bounds on the performance of our scheme, and evaluate it experimentally with real-world data.
UR - http://www.scopus.com/inward/record.url?scp=38049161055&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38049161055&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-71701-0_124
DO - 10.1007/978-3-540-71701-0_124
M3 - Conference contribution
AN - SCOPUS:38049161055
SN - 9783540717003
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1096
EP - 1104
BT - Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings
PB - Springer Verlag
T2 - 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
Y2 - 22 May 2007 through 25 May 2007
ER -