A clustering approach for anonymizing distributed data streams
Mona A. Mohamed, Magdy H. Nagi, Sahar Mohamed Ghanem · 2016
Privacy preserving data mining have been studied widely on static data. Static algorithms are not suitable for streaming data. This imposes the study of new algorithms for privacy preserving that cope with data streams characteristics. Recently, effective anonymization algorithms have been studied on centralized data streams. In this paper we propose an approach for anonymizing distributed data streams based on clustering. First, anonymization is performed locally at each site by clustering a single stream, then local clusters are exchanged between sites through a global server to construct global clusters. The algorithm is shown to be effective when compared to a centralized algorithm and to the case where no communication is exchanged between sites. In addition, empirical results on real and synthetic data sets have shown that the proposed algorithm gives better information loss when compared to the without communication case and close results to the centralized case. Moreover, the algorithm is shown to be efficient in terms of communication and scalable with increasing number of sites.