Towards Secure Privacy Preserving Data Mining over Computational Grids
نویسندگان
چکیده
Grid computing facilitates the realization of large-scale intraand inter-organization collaborative computer applications by harnessing computing, storage, and networking resources available over the Internet. The concept of grid computing paradigm is analogous to that of electricity power grid where electricity sources are connected together in a grid and consumes’ needs for electricity are addressed by providing ubiquitous access to this grid. Among the most promising applications that can reap the full benefits of grid-computing paradigm is distributed knowledge discovery, also referred to as distributed data mining, where large data sets across multiple and geographically distributed administrative domains are involved. However in order for grid computing to be successful for data mining applications, where classified data across organizations may be shared, security and privacy issues need a more careful review. In this paper, we first examine the recently proposed layered architecture for the computation grid and explore grid security infrastructure (GSI) provided in Globus Toolkit, one of the most popular collections of resource and connectivity protocols and APIs for the grid. We then extend the GSI to support Privacy Preserving Data Mining (PPDM) over grids and elaborate on future research issues related to secure computing over grids.
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