Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2018

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, Cloud computing, Frequent itemset mining, Privacy preserving outsourcing

Discipline

Data Storage Systems | Information Security

Publication

Proceedings of 23rd Australasian Conference on Information Security and Privacy, Wollongong, Australia, 2018 July 11-13

First Page

431

Last Page

451

ISBN

9783319936376

Identifier

10.1007/978-3-319-93638-3_25

Publisher

Springer Verlag

City or Country

Wollongong, Australia

Additional URL

https://doi.org/10.1007/978-3-319-93638-3_25

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