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Erschienen in:
2010 | OriginalPaper | Buchkapitel
eM2: An Efficient Member Migration Algorithm for Ensuring k-Anonymity and Mitigating Information Loss
verfasst von : Phuong Huynh Van Quoc, Tran Khanh Dang
Erschienen in: Secure Data Management
Verlag: Springer Berlin Heidelberg
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Privacy preservation (PP) has become an important issue in the information age to prevent expositions and abuses of personal information. This has attracted much research and k-anonymity is a well-known and promising model invented for PP. Based on the k-anonymity model, this paper introduces a novel and efficient member migration algorithm, called eM
2
, to ensure k-anonymity and avoid information loss as much as possible, which is the crucial weakness of the model. In eM
2
, we do not use the existing generalization and suppression technique. Instead we propose a member migration technique that inherits advantages and avoids disadvantages of existing k-anonymity-based techniques. Experimental results with real-world datasets show that eM
2
is superior to other k-anonymity algorithms by an order of magnitude.