Paper Info
Title
Privacy-Preserving Mining Of Association Rule On Outsourced Cloud Data From Multiple Parties
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.
Year
DOI
Venue
2018
10.1007/978-3-319-93638-3_25
INFORMATION SECURITY AND PRIVACY
Keywords
Field
DocType
Association rule mining, Frequent itemset mining, Privacy preserving outsourcing, Cloud computing
Architecture,Secure multi-party computation,Confidentiality,Computer security,Computer science,Upload,Encryption,Theoretical computer science,Association rule learning,Information privacy,Cloud computing
Conference
Volume
ISSN
Citations 
10946
0302-9743
0
PageRank 
References 
Authors
0.34
14
7
Name
Order
Citations
PageRank
Lin Liu11128115.75
Su, Jinshu275096.41
Rongmao Chen312918.68
Ximeng Liu430452.09
Xiaofeng Wang5989.41
Shuhui Chen63110.38
hofung leung71314132.32