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Efficient Data Transfer by using Clustering Techniques in Castle

A. Elangeswari, R. Prabu


Data anonymization techniques based on k-Anonymity model have been the focus of intense research in the last few years. Although the k-Anonymity model provides valuable solutions to privacy-preserving, current solution are limited to the static data release (i.e.., the entire data set is assumed to be available at the time of release).Today as database continuously growing every day and even every hour. In such streaming applications, there is need to offer strong guarantees on the maximum allowed delay between incoming data and the corresponding anonymized output. To cope with these requirements CASTLE(Continuously Anonym zing Streaming data via adaptive cLustEring) a cluster based scheme is introduced that anonymized data streams and ensures the freshness of the anonymized data by satisfying specified delay constraints. In addition CASTLE has been extended to l-diversity requires that each equivalence class has at least l well represented values for each sensitive attributes.


Data Stream, Privacy-preserving Data Mining, Anonymity

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