Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
| Hi-index | 0.00 |
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%.