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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Verifiable and Privacy-Preserving Association Rule Mining in Hybrid Cloud Environment
verfasst von : Hong Rong, Huimei Wang, Jian Liu, Fengyi Tang, Ming Xian
Erschienen in: Green, Pervasive, and Cloud Computing
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Abstract
Nowadays it is becoming a trend for data owners to outsource data storage together with their mining tasks to cloud service providers, which however brings about security concerns on the loss of data integrity and confidentiality. Existing solutions seldom protect data privacy whilst ensuring result integrity. To address these issues, this paper proposes a series of privacy-preserving building blocks. Then an efficient verifiable association rule mining protocol is designed under hybrid cloud environment, in which the public unreliable cloud and semi-honest cloud collaborate to mine frequent patterns over the encrypted database. Our scheme not only protects the privacy of datasets from frequency analysis attacks, but also verifies the correctness of mining results. Theoretical analysis demonstrates that the scheme is semantically secure under the threat model. Experimental evaluations show that our approach can effectively detect faulty servers while achieving good privacy protection.