Rotation Perturbation Techniques in Privacy Preserving Data Mining with clustering Approach

Data Mining is the process of finding the interesting Knowledge from Large Amount of data stored in database, data warehouse or other information repositories. In data mining regularly data collected and analyzed by organizations and governments. For this database we have to provide Privacy, confidentiality and security. In Privacy Preserving data mining we have the many traditional technique these technique are not giving the accurate result. In preserving Privacy of individuals when data are shared for clustering it is complex problem. The challenge is how to protect the underlying attribute values subjected to clustering without Jeopardizing similarity between data object under analysis. To address this problem data owner must not only require privacy, but also guarantee for valid clustering result. To achieve this dual goal we propose the rotation based transformation .These approach is based on principal component analysis which is exiting and it is extended to discrete cosine transformation to achieve privacy. The success of privacy can be measure in terms of data utility, Performance resistance and level of uncertain to data mining algorithm etc.

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