Privacy-Preserving Mining of Association Rule on Outsourced Cloud Data from Multiple Parties
Refereed conference paper presented and published in conference proceedings

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AbstractIt 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.
All Author(s) ListLin Liu, Jinshu Su, Rongmao Chen, Ximeng Liu, Xiaofeng Wang, Shuhui Chen, Ho-fung Leung
Name of Conference23rd Australasian Conference on Information Security and Privacy (ACISP 2018)
Start Date of Conference11/07/2018
End Date of Conference13/07/2018
Place of ConferenceWollongong
Country/Region of ConferenceAustralia
Proceedings TitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume Number10946
Pages431 - 451
LanguagesEnglish-United States
KeywordsAssociation rule mining, Frequent itemset mining, Privacy preserving outsourcing, Cloud computing

Last updated on 2020-05-08 at 04:33