Privacy-Preserving and K- Nearest Means Clustering over Relational Data

Data Mining has wide use in many fields such as financial, medication, medical research and among govt. departments. Classification is one of the widely applied works in data mining applications. For the past several years, due to the increase of various privacy problems, many conceptual and realistic alternatives to the classification issue have been suggested under various protection designs. On the other hand, with the latest reputation of cloud processing, users now have to be able to delegate their data, in encoded form, as well as the information mining task to the cloud. Considering that the information on the cloud is in secured type, current privacy-preserving classification methods are not appropriate. In this paper, we concentrate on fixing the classification issue over encoded data. In specific, we recommend a protected k-classifier over secured data in the cloud. The suggested protocol defends the privacy of information, comfort of user’s feedback query, and conceals the information access styles. To the best of our information, our task is the first to create a protected kclassifier over secured data under the semi-honest model. Also, we empirically evaluate the performance of our suggested protocol utilizing a real-world dataset under various parameter configurations. To secure user privacy, numerous privacy-preserving category methods have been suggested over the past several years. The current methods are not appropriate to contracted database surroundings where the information exists in secured form on a third-party server