A Novel Privacy- Preserving K-NN Classification Protocol Over Encrypted DataA Novel Privacy- Preserving K-NN Classification Protocol Over Encrypted Data

International Journal of Advance Foundation and Research in Computer (IJAFRC)

Volume 2, Issue 10, October - 2015. ISSN 2348 4853, Impact Factor 1.317

1 | 2015, IJAFRC All Rights Reserved www.ijafrc.org

A Novel Privacy- Preserving K-NN Classification Protocol

Over Encrypted Data Mr. Kumud Ranjan Kumar, Mr. Ankush Prasun, Mr. Nityanand, Ms. Anjali Jadhav

Imperial College of Engineering and Research, Pune,Maharashtra India

[email protected], [email protected]

A B S T R A C T

Data mining is used for various ways like government sectors, private sectors eg. Banking,

Research center. Data mining is the classification process. Now a days, due to the growth of

Internet various privacy issues arise. In classification problem different security model will be

prepared. Now a days, due to the growth of cloud computing, user can outsource their data on

cloud in encrypted form and can apply data mining tasks on cloud. In data mining privacy-

preserving classification technique are not applicable. So, in our paper we are solving the privacy-

preserving classification problem over encrypted data. We are using k-NN classifier technique

over encrypted data in cloud. The proposed system provides-Confidentiality of data, Privacy of

user input query, Hides data access patterns

Index Terms : k-NN Classifier, AES algorithm, Normalization.

I. INTRODUCTION

The DMED (Data mining over encrypted data) problem is not solve in Data mining. In this paper, we are

proposed to solve the DMED problem on cloud. It also protects the data of user when record is apart of a

Data mining process.

Cloud can be used for getting the useful and sensitive (Private data) information from the actual data by

keeping the eyes on pattern used by the user even if the data is in the encrypted for. The Privacy-

preserving issue is solved in cloud, it provides-

(1) Confidentiality of the encrypted data.

(2) Confidentiality of a users query record.

(3) Hiding data access patterns.

The proposed system is focuses on executing k-nearest neighbor classification Technique over encrypted

data in the cloud computing environment.

II. Explanation:

In this paper, we are using k-nearest neighbor classification technique for solving the DMED problem.

Normalization technique and AES algorithm is using for encryption of data. The proposed system is

providing privacy-Preserving classification over encrypted data on Cloud.

A. Normalization Technique:




Normalization is the process of managing tables and column for minimizing redundant data.

Normalization may have the effect of duplicating data within the database and often results in the

creation of additional tables.

B. K-nearest neighbor classification technique:

K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on

a similarity measure (e.g., distance functions).

Algorithm:

A case is classified by a majority vote of its neighbours, with the case being assigned to the class most

common amongst its K nearest neighbours measured by a distance function. If K = 1, then the case is

simply assigned to the class of its nearest neighbour.

Example:

Consider the following data concerning credit default. Age and Loan are two numerical variables

(predictors) and Default is the target.




We can now use the training set to classify an unknown case (Age=48 and Loan=$142,000) using Euclidean distance.

If K=1 then the nearest neighbor is the last case in the training set with Default=Y.

D = Sqrt[(48-33)^2 + (142000-150000)^2] = 8000.01 >> Default=Y

With K=3, there are two Default=Y and one Default=N out of three closest neighbors. The prediction for

The unknown case is again Default=Y.

C. AES algorithm:

AES (acronym of Advanced Encryption Standard) is a symmetric encryption algorithm. The block to be

encrypted is just a sequence of 128 bits. AES works with byte quantities so we first convert the 128 bits

into 16 bytes.




AES Block Diagram:

The encryption process uses a set of specially derived keys called round keys. These are applied, along

with other operations, on an array of data that holds exactly one block of data. The data to be encrypted.

This array we call the state array.

AES Steps of encryption for a 128-bit block:

1. Derive the set of round keys from the cipher key.

2. Initialize the state array with the block data (plaintext).

3. Add the initial round key to the starting state array.

4. Perform nine rounds of state manipulation.

5. Perform the tenth and final round of state manipulation.

6. Copy the final state array out as the encrypted data (ciphertext).

III. Architecture Diagram:




In this system, User inputs data to server i.e. user upload data on server after that we use data mining

process for data part ion we calculate mean and deviation after that normalize data. Server do the

encryption of that data then when user demand data server decrypt that data after that we apply the K-

NN algorithm and we improve the classification of data.

IV. Flow Diagram

All the data will be first stored in to data set until it gets loaded. Partition will be done on the basis of

their individual characteristic of the data. Mean and standard deviation will take place after

Normalization. Encryption will be carried out on Normalized data. Encrypted data can be decrypted by

The authorized user and the related data can be searched by using K-nearest neighbor algorithm. After

that some accurate data will find.




V. CONCLUSION

The proposed system provides the user privacy. It also solves the DMED problem using k-NN

classification technique over encrypted data n cloud. A novel privacy- Preserving k-NN classification

protocol over encrypted data in cloud

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A Novel Privacy- Preserving K-NN Classification Protocol Over Encrypted DataA Novel Privacy- Preserving K-NN Classification Protocol Over Encrypted Data