Journal article
Fully homomorphic encryption based two-party association rule mining
Data & Knowledge Engineering, Vol.76-78, pp.1-15
2012
Abstract
Association rule mining (ARM) is one of the popular data mining methods that discover interesting correlations amongst a large collection of data, which appears incomprehensible. This is known to be a trivial task when the data is owned by one party. But when multiple data sites collectively engage in ARM, privacy concerns are introduced. Due to this concern, privacy preserving data mining algorithms have been developed to attain the desired result, while maintaining privacy. In the case of two party privacy preserving ARM for horizontally partitioned databases, both parties are required to compare their itemset counts securely. This problem is comparable to the famous millionaire problem of Yao. However, in this paper, we propose a secure comparison technique using fully homomorphic encryption scheme that provides a similar level of security to the Yao based solution, but promotes greater efficiency due to the reuse of resources.
Details
- Title
- Fully homomorphic encryption based two-party association rule mining
- Authors/Creators
- M.G. Kaosar (Author/Creator)R. Paulet (Author/Creator)X. Yi (Author/Creator)
- Publication Details
- Data & Knowledge Engineering, Vol.76-78, pp.1-15
- Publisher
- Elsevier BV
- Identifiers
- 991005541766307891
- Copyright
- © 2012 Elsevier B.V.
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.187 Security Systems
- 4.187.160 Cryptographic Protocols
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- ESI research areas
- Computer Science