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access icon free Privacy preserving big data mining: association rule hiding using fuzzy logic approach

© The Institution of Engineering and Technology
Received 04/12/2015, Accepted 15/06/2017, Revised 26/09/2016, Published 06/07/2017

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.

Inspec keywords: data protection; fuzzy logic; Big Data; data mining; authorisation

Other keywords: association rule hiding; sensitive association rule; continuous data; parallelisation techniques; static databases; data anonymisation; streaming data; association rule mining; privacy preserving Big Data mining; membership degree; fuzzy logic approach

Subjects: Other DBMS; Knowledge engineering techniques; Data security

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