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We present a secure design for a map-reduce framework that guarantees preservation of privacy of the original data. We use Hadoop as a typical environment for illustration. That is, in spite of the data divisions/replications for the computations, the privacy of the original data remains invariant. Specifically, we use the novel information flow model called RWFM model that assures that in spite of...
This paper addresses the selection of sub-feature from each feature using fuzzy methodologies maintaining the privacy during collection of data from participating parties in distributed environment. Based on fuzzy random variables conditional expectation is used in which two fuzzy sets are generated using Borel set that helps to determine sub-feature within certain interval. The privacy and selection...
Privacy policies are often defined in terms of purposes for which the information may be used. Recent work on specification and enforcement of purpose-based privacy policies proceeds by expressing purpose constraints using modal logics for defining when a sequence of actions is "only for" and "not for" a purpose, where a purpose is modelled using either Marko decision processes...
The feature selection addresses the issue of developing accurate models for classification in data mining. The aggregated data collection from distributed environment for feature selection makes the problem of accessing the relevant inputs of individual data records. Preserving the privacy of individual data is often critical issue in distributed data mining. In this paper, it proposes the privacy...
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