ImDMI: Improved Distributed M-Invariance model to achieve privacy continuous big data publishing using Apache Spark

Abstract

One of the critical challenges in the big data analytics is the individual's privacy issues. Data anonymization models including k-anonymity and l-diversity are used to guarantee the tradeoff between privacy and data utility while publishing the data. However, these models focus only on the single release of datasets and produce a certain level of privacy. In practical big data applications, data publishing is more complicated where the data is published continuously as new data is collected, and the privacy should be achieved for different releases. In this research, we propose a new distributed bottom up approach on Apache Spark for achievement of the m-invariance privacy model in the continuous big data context. The proposed approach, which is the first study that deals with dynamic big data publishing, is based on the insertion and the split process. In the first process, the data records collected from different workers are inserted into an improved bottom up R-tree generalization in order to minimizing the information loss. The second process concentrates on splitting the overflowed node with respect to the m-invariance model requirement by minimizing the overlap between the resulting partitions. The experimental results show significant improvement in term of data utility, execution time and counterfeit data records as compared to existing techniques in the literature.