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Privacy-Preserving Mining of Association Rule on Outsourced Cloud Data from Multiple Parties

  • Lin Liu
  • Jinshu SuEmail author
  • Rongmao ChenEmail author
  • Ximeng Liu
  • Xiaofeng Wang
  • Shuhui Chen
  • Hofung Leung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10946)

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.

Keywords

Association rule mining Frequent itemset mining Privacy preserving outsourcing Cloud computing 

Notes

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.National Key Laboratory for Parallel and Distributed ProcessingNational University of Defense TechnologyChangshaChina
  3. 3.School of Information SystemsSingapore Management UniversitySingaporeSingapore
  4. 4.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina
  5. 5.Department of Computer Science and EngineeringChinese University of Hong KongShatinHong Kong

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