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

Category : Data Mining


Sub Category : JAVA


Project Code : ITJDM01


Project Abstract

 

K-Nearest Neighbor Classification over Semantically Secure

Encrypted Relational Data

 

ABSTRACT:-

 

Data Mining: Data mining is extract data from in the Database, the data mining 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. 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 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.

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

Data perturbation technique to build a decision-tree classifier, data perturbation techniques cannot be applicable for semantically secure encrypted data.

PROPOSED CONCEPT: -

This paper concentrates on executing the k-nearest neighbor classification method over encrypted data in the cloud computing environment.

Provides formal security proofs of the underlying sub-protocols as well as the PPkNN protocol under the semi-honest model.

EXISTING TECHNIQUE:-

Privacy-preserving classification technique.

PROPOSED TECHNIQUE:-

Novel secure k-Nearest Neighbor Classification (K-NN).

TECHNIQUE DEFINITION:-

Data perturbation technique to build a decision-tree classifier, data perturbation techniques cannot be applicable for semantically secure encrypted data.

TECHNIQUE DEFINITION:-

Novel secure k-nearest neighbor query protocol over encrypted data that protects data confidentiality, user’s query privacy and hides data access patterns.

DRAWBACKS:-

Existing work on privacy-preserving data mining (PPDM) (either perturbation or secure multi-party computation (SMC) based approach) cannot solve the DMED problem.

Perturbed data do not possess semantic security, so data perturbation techniques cannot be used to encrypt highly sensitive data. Also the perturbed data do not produce very accurate data mining results.

ADVANTAGES:-

We introduced new security primitives, namely secure minimum (SMIN), secure minimum out of numbers (SMINn), secure frequency (SF), and proposed new solutions for them.

Provides formal security proofs of the underlying sub-protocols as well as the PPkNN protocol under the semi-honest model.

 
 
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