TY - GEN
T1 - Protecting privacy in incremental maintenance for distributed association rule mining
AU - Wong, W. K.
AU - Cheung, David W.
AU - Hung, Edward
AU - Liu, Huan
PY - 2008
Y1 - 2008
N2 - Distributed association rule mining algorithms are used to discover important knowledge from databases. Privacy concerns can prevent parties from sharing the data. New algorithms are required to solve traditional mining problems without disclosing (original or derived) information of their own data to other parties. Research results have been developed on (i) incrementally maintaining the discovered association rules, and (ii) computing the distributed association rules while preserving privacy. However, no study has been conducted on the problem of the maintenance of the discovered rules with privacy protection when new sites join the old sites. We propose an algorithm SIMDAR for this problem. Some techniques we developed can even further reduce the cost in a normal association rule mining algorithm with privacy protection. Experimental results showed that SIMDAR can significantly reduce the workload at the old sites by up to 80%.
AB - Distributed association rule mining algorithms are used to discover important knowledge from databases. Privacy concerns can prevent parties from sharing the data. New algorithms are required to solve traditional mining problems without disclosing (original or derived) information of their own data to other parties. Research results have been developed on (i) incrementally maintaining the discovered association rules, and (ii) computing the distributed association rules while preserving privacy. However, no study has been conducted on the problem of the maintenance of the discovered rules with privacy protection when new sites join the old sites. We propose an algorithm SIMDAR for this problem. Some techniques we developed can even further reduce the cost in a normal association rule mining algorithm with privacy protection. Experimental results showed that SIMDAR can significantly reduce the workload at the old sites by up to 80%.
UR - http://www.scopus.com/inward/record.url?scp=44649113751&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=44649113751&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-68125-0_34
DO - 10.1007/978-3-540-68125-0_34
M3 - Conference contribution
AN - SCOPUS:44649113751
SN - 3540681248
SN - 9783540681243
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 381
EP - 392
BT - Advances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings
T2 - 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008
Y2 - 20 May 2008 through 23 May 2008
ER -