DATA MINING OVER ENCRYPTED DATA ON CLOUD

Abstract

Data Mining has wide applications in many areas such as medicine, scientific, banking, research and among government agencies. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. Classification is one of the commonly used tasks in data mining applications However, with the recent popularity of cloud computing, users now have the opportunity 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 techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed protocol protects the confidentiality of data, privacy of user’s input query, and hides the data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our proposed protocol using a real-world dataset under different parameter settings. The proposed system mainly focuses on information security in insurance company. They can encrypt the customer information and stored it in database. When data are encrypted, any data mining tasks becomes very challenging before decrypting data. Classification can apply to the customer records. This protects the customers’ sensitive information.