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Data Mining has wide applications in many areas such as banking, medicine,scientific research and among government agencies. Classification is one of thecommonly used tasks in data mining applications. For the past decade, due tothe rise of various privacy issues, many theoretical and practical solutions tothe classification problem have been proposed under different security models.However, with the recent popularity of cloud computing, users now have theopportunity to outsource their data, in encrypted form, as well as the datamining tasks to the cloud. Since the data on the cloud is in encrypted form,existing privacy preserving classification techniques are not applicable. Inthis paper, we focus on solving the classification problem over encrypted data.In particular, we propose a secure k-NN classifier over encrypted data in thecloud. The proposed k-NN protocol protects the confidentiality of the data,user's input query, and data access patterns. To the best of our knowledge, ourwork is the first to develop a secure k-NN classifier over encrypted data underthe semi-honest model. Also, we empirically analyze the efficiency of oursolution through various experiments.

k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data