PROJECT TITLE:

k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data - 2015

ABSTRACT:

Knowledge Mining has wide applications in several areas like banking, medication, scientific research and among government agencies. Classification is one amongst the commonly used tasks in knowledge mining applications. For the past decade, thanks to the increase of various privacy problems, several theoretical and sensible solutions to the classification drawback are proposed below completely different security models. However, with the recent popularity of cloud computing, users currently have the chance to outsource their information, in encrypted type, plus the info mining tasks to the cloud. Since the information on the cloud is in encrypted type, existing privacy-preserving classification techniques aren't applicable. In this paper, we have a tendency to specialise in solving the classification problem over encrypted knowledge. In explicit, we tend to propose a secure k-NN classifier over encrypted knowledge within the cloud. The proposed protocol protects the confidentiality of data, privacy of user's input query, and hides the info access patterns. To the simplest of our information, our work is the primary to develop a secure k-NN classifier over encrypted knowledge below the semi-honest model. Also, we empirically analyze the efficiency of our proposed protocol employing a real-world dataset below totally different parameter settings.