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k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies.
Abstract-Synopsis-Documentation

k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Abstract:

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. 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. 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.

Existing System:

Ensuring the security of data is therefore critical not only to preserve the data’s of employees’ highly personal information, but also to minimize the legal risk to the organization as a whole. When some organizations not view the full datails of the job seekers CV.so for we have to provide the security for this CV. When an organization takes care of reduce the manual workload an organization performs, they choose to replace those processes with various levels of security systems.

Proposed System:

we propose a secure k-NN classifier over encrypted data in the cloud. 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, our work is the first to develop a secure k-NN classifier over encrypted data under the standard semi-honest model. Also, we empirically analyze the efficiency of our solution through various experiments.

Algorithums:

1.k-means clustering algorithum 2. ElGamal Algorithums

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