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Privacy Preserving Clustering on Distorted data
2012
IOSR Journal of Computer Engineering
In designing various security and privacy related data mining applications, privacy preserving has become a major concern. Protecting sensitive or confidential information in data mining is an important long term goal. An increased data disclosure risks may encounter when it is released. Various data distortion techniques are widely used to protect sensitive data; these approaches protect data by adding noise or by different matrix decomposition methods. In this paper we primarily focus, data
doi:10.9790/0661-0522529
fatcat:ya6li4lftbbttlmf6flhnhon7u