Open AccessPrivacy preserving big data mining: association rule hiding using fuzzy logic approach
Author(s) -
Afzali Golnar Assadat,
Mohammadi Shahriar
Publication year - 2018
Publication title -
iet information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 34
eISSN - 1751-8717
pISSN - 1751-8709
DOI - 10.1049/iet-ifs.2015.0545
Subject(s) - association rule learning , computer science , data mining , big data , executable , fuzzy logic , process (computing) , volume (thermodynamics) , artificial intelligence , physics , quantum mechanics , operating system
Recently, privacy preserving data mining has been studied widely. Association rule mining can cause potential threat toward privacy of data. So, association rule hiding techniques are employed to avoid the risk of sensitive knowledge leakage. Many researches have been done on association rule hiding, but most of them focus on proposing algorithms with least side effect for static databases (with no new data entrance), while now the authors confront with streaming data which are continuous data. Furthermore, in the age of big data, it is necessary to optimise existing methods to be executable for large volume of data. In this study, data anonymisation is used to fit the proposed model for big data mining. Besides, special features of big data such as velocity make it necessary to consider each rule as a sensitive association rule with an appropriate membership degree. Furthermore, parallelisation techniques which are embedded in the proposed model, can help to speed up data mining process.