Fast Retrieval of Information over Encrypted Cloud Data using Hybrid k-nearest Neighbor
Now a day’s data mining has been used in many fields such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the last two decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security tasks. However, todays demand of cloud computing, users now have the big scope to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy preserving classification methods or techniques are not applicable. In this paper, proposed a secure k-NN classifier model over the encrypted data in the cloud. The proposed k-NN protocol protects the confidentiality of the data, user’s input query, and data access patterns. The aim of our proposed work is the first to develop a secure k-NN classifier over encrypted data under the standard semi-honest model.
Index terms - k-NN classifier, data mining, privacy preserving.