Visible to the public Data mining for privacy preserving association rules based on improved MASK algorithm

TitleData mining for privacy preserving association rules based on improved MASK algorithm
Publication TypeConference Paper
Year of Publication2014
AuthorsHaoliang Lou, Yunlong Ma, Feng Zhang, Min Liu, Weiming Shen
Conference NameComputer Supported Cooperative Work in Design (CSCWD), Proceedings of the 2014 IEEE 18th International Conference on
Date PublishedMay
KeywordsAlgorithm design and analysis, association rules, data mining, data perturbation and query restriction, data perturbation strategy, data privacy, DPQR algorithm, improved MASK algorithm, Information Privacy, inverse matrix, Itemsets, matrix algebra, mining associations with secrecy konstraints, multi-parameters perturbation, privacy preservation, privacy preserving association rules, query processing, scanning database, security issues, Time complexity
Abstract

With the arrival of the big data era, information privacy and security issues become even more crucial. The Mining Associations with Secrecy Konstraints (MASK) algorithm and its improved versions were proposed as data mining approaches for privacy preserving association rules. The MASK algorithm only adopts a data perturbation strategy, which leads to a low privacy-preserving degree. Moreover, it is difficult to apply the MASK algorithm into practices because of its long execution time. This paper proposes a new algorithm based on data perturbation and query restriction (DPQR) to improve the privacy-preserving degree by multi-parameters perturbation. In order to improve the time-efficiency, the calculation to obtain an inverse matrix is simplified by dividing the matrix into blocks; meanwhile, a further optimization is provided to reduce the number of scanning database by set theory. Both theoretical analyses and experiment results prove that the proposed DPQR algorithm has better performance.

DOI10.1109/CSCWD.2014.6846853
Citation Key6846853