Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Computer Science: An Overview (9th Edition)
Computer Science: An Overview (9th Edition)
Digital Design and Computer Architecture
Digital Design and Computer Architecture
Privacy-preserving collaborative association rule mining
Journal of Network and Computer Applications
Fully homomorphic encryption using ideal lattices
Proceedings of the forty-first annual ACM symposium on Theory of computing
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Fully homomorphic encryption over the integers
EUROCRYPT'10 Proceedings of the 29th Annual international conference on Theory and Applications of Cryptographic Techniques
Optimized two party privacy preserving association rule mining using fully homomorphic encryption
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part I
Secure Two-Party Association Rule Mining Based on One-Pass FP-Tree
International Journal of Information Security and Privacy
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Association rule mining algorithm provides a means for determining rules and patterns from a large collection of data. However, when two sites want to engage in an association rule mining, data privacy concerns are raised. These concerns include loosing a competitive edge in the market place and breaching privacy laws. Techniques that have addressed this problem are data perturbation and homomorphic encryption. Homomorphic encryption based solutions produce more accurate results than data perturbation. Most previous solutions for privacy preserving association rule mining require the disclosure of intermediate mining results such as support counts and database size to determine frequent itemset. To overcome this weakness we propose a secure comparison technique based on state-of-the-art fully homomorphic encryption scheme, by which we build secure two-party association rule mining protocol. Our solution preserves complete privacy of both parties and it is more efficient than other solutions because there is no need for exponentiation of numbers.