Abstract
With the advent of the big data era, outsourcing data storage together with data mining tasks to cloud service providers is becoming a trend, which however incurs security and privacy issues. To address the issues, this paper proposes two protocols for mining frequent patterns securely on the cloud by employing fully homomorphic encryption. One protocol requires little communication between the client and the cloud service provider, the other incurs less computation cost. Moreover, a new privacy notion, namely \(\alpha \)-pattern uncertainty, is proposed to reinforce the second protocol. Our scenario has two advantages: one is stronger privacy protection, and the other is that the outsourced data can be used in different mining tasks. Experimental evaluation demonstrates that the proposed protocols provide a feasible solution to the issues.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (61272306), and the Zhejiang Provincial Natural Science Foundation of China (LY12F02024).
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Liu, J., Li, J., Xu, S., Fung, B.C. (2015). Secure Outsourced Frequent Pattern Mining by Fully Homomorphic Encryption. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science(), vol 9263. Springer, Cham. https://doi.org/10.1007/978-3-319-22729-0_6
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DOI: https://doi.org/10.1007/978-3-319-22729-0_6
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