Abstract
It has been widely recognized as a challenge to carry out data analysis and meanwhile preserve its privacy in the cloud. In this work, we mainly focus on a well-known data analysis approach namely association rule mining. We found that the data privacy in this mining approach have not been well considered so far. To address this problem, we propose a scheme for privacy-preserving association rule mining on outsourced cloud data which are uploaded from multiple parties in a twin-cloud architecture. In particular, we mainly consider the scenario where the data owners and miners have different encryption keys that are kept secret from each other and also from the cloud server. Our scheme is constructed by a set of well-designed two-party secure computation algorithms, which not only preserve the data confidentiality and query privacy but also allow the data owner to be offline during the data mining. Compared with the state-of-art works, our scheme not only achieves higher level privacy but also reduces the computation cost of data owners.
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Notes
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For example, if \( \varvec{f }= \{1,1,1,0\} \) which means \(\{X,Y,Z\}\). The sets of \(\varvec{f }\)’s nonvoid proper subset is \(H = \{\{X\},\{Y\} \, \{Z\}, \{X, Y\}, \{X,Z\}, \{Y, Z\} \}\), which can be represent as
$$\begin{aligned} H = \{\{1,0,0,0\},\{0,1,0,0\}, \{0,0,0,1\},\{1,1,0,0\}, \{1,0,1,0\}, \{0,1,1,0\}\}. \end{aligned}$$.
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Acknowledgement
The authors would like to thank Dr. Shuo Qiu for her generous feedback. The work is supported by the National Natural Science Foundation of China (No. 61702541, No. 61702105), the Young Elite Scientists Sponsorship Program by CAST (2017QNRC001), the Science and Technology Research Plan Program by NUDT (Grant No. ZK17-03-46), the national key research and development program under grant 2017YFB0802301, and Guangxi cloud computing and large data Collaborative Innovation Center Project.
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Liu, L. et al. (2018). Privacy-Preserving Mining of Association Rule on Outsourced Cloud Data from Multiple Parties. In: Susilo, W., Yang, G. (eds) Information Security and Privacy. ACISP 2018. Lecture Notes in Computer Science(), vol 10946. Springer, Cham. https://doi.org/10.1007/978-3-319-93638-3_25
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