In recent years, a new research area known as privacy preserving data mining (PPDM) has emerged and captured the attention of many researchers interested in preventing the privacy violations that may occur during data mining. In this paper, we provide a review of studies on PPDM in the context of association rules (PPARM). This paper systematically defines the scope of this survey and determines the PPARM models. The problems of each model are formally described, and we discuss the relevant approaches, techniques and algorithms that have been proposed in the literature. A profile of each model and the accompanying algorithms are provided with a comparison of the PPARM models.
The increasing amount of data in many web content systems has resulted in interesting opportunities for data driven knowledge discovery and data mining techniques. The aim of this paper is to propose a web content distributed data mining model) to get the benefits from these opportunities. The proposed model tries to overcome the expected difficulties which are related to these kinds of data. Distributed nature, heterogeneous data, privacy, data learning, and increasing interoperability within secured communications and performance issues are also considered
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