A secured framework to protect association rules in the big data environment using fuzzy logic

Abstract

Data security is an important issue in the age of big data. The existing data security approaches should be improved to cover inactive databases, i.e. the databases with existing information only, and suit the requirements of big data mining. Therefore, this paper proposes framework to protect the data anonymity in big data environment. The framework is mainly implemented in three steps: mining the association rules, computing the confidence of each rule, and determining the sensitivity of each rule using fuzzy logic. To process massive data, the authors paid attention to enhance the parallelism and scalability of the proposed framework. The proposed framework was verified through experiments on two datasets. Judging by metrics like lost, ghost and false rules, it is confirmed that our framework can protect the association rules efficiently in the big data environment.