On Unifying Privacy and Uncertain Data Models

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On Unifying Privacy and Uncertain Data Models
The problem of privacy-preserving data mining has been studied extensively in recent years because of the increased amount of personal information which is available to corporations and individuals. Most privacy transformations use some form of data perturbation or representational ambiguity in order to reduce the risk of identification. The final results from privacy transformation methods often require the underlying applications to be modified in order to work with the new representation of the data. Since the end results of privacy-transformation methods have not been standardized, the required modifications may vary with the method used for the privacy transformation. In some cases, it can be an enormous effort to re-design applications to work with the anonymized data. While the results of privacy-transformation methods are a natural form of uncertain data, the two problems have generally been studied independently. In this paper, we make a first attempt to unify the two fields, ...
Charu C. Aggarwal
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2008
Where ICDE
Authors Charu C. Aggarwal
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