Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
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
A new efficient privacy-preserving scalar product protocol
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
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Recently, privacy issues have becomes important in data mining, especially when data is horizontally or vertically partitioned. For the vertically partitioned case, many data mining problems can be reduced to securely computing the scalar product. Among these problems, we can mention association rule mining over vertically partitioned data. Efficiency of a secure scalar product can be measured by the overhead of communication needed to ensure this security. Several solutions have been proposed for privacy preserving association rule mining in vertically partitioned data. But the main drawback of these solutions is the excessive overhead communication needed for ensuring data privacy. In this paper we propose a new secure scalar product with the aim to reduce the overhead communication.