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.

1. K NEAREST NEIGHBOUR CLASSIFICATION OVER
SEMANTICALLY SECURE ENCRYPTED RELATIONAL
DATA
By
V Lakshmi

2. CONTENTS
 Abstract
 Problem Statement
 Existing System
 Proposed System
 Disadvantages
 Advantages
 System Specifications
 UML diagrams
 Implementation
 Results
 Conclusion

3. 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.

4. PROBLEM STATEMENT:
 To Focus on solving the classification problem
over Encrypted data
EXISTING SYSTEM:
 The system is implemented fully homomorphic cryptosystems
can solve the DMED problem.
 since it allows a third-party (that hosts the encrypted data) to
execute arbitrary functions over encrypted data without ever
decrypting them.

5. PROPOSED SYSTEM:
 The system proposed novel methods to effectively
solve the DMED problem assuming that the
encrypted data are outsourced to a cloud.
 The system focuses on the classification problem
since it is one of the most common data mining
tasks.

6. ADVANTAGES :
 It protects the confidentiality of data, privacy of
user’s input query.
 Hides data access patterns.
 Data records correspond to the k-nearest
neighbours and the output class label are not
known to the cloud.
DISADVANTAGES:
 Perturbed data do not possess semantic security.

7. SYSTEM SPECIFICATION
Hardware Requirements:
 System : Pentium IV 3.4 GHz.
 Hard Disk : 40 GB.
 Floppy Drive : 1.44 Mb.
 Ram : 1 GB.
Software Requirements:
 Operating system : Windows 7
 Coding Language : J2EE(JSP,Servlet,JavaBean)
 Data Base : MY Sql Server.
 Web Server : Tomcat 6.0

8. FUNCTIONAL REQUIREMENTS :
Data Owner :
 Login
 Upload Data
 View Data and features
 Add common Questions construction
Data Server :
o view all files
o View attacks
o Encrypted files
USER
o Registration
 Login
 View and choose Data features

9. NON-FUNCTIONAL REQUIREMENTS
 Usability
 Reliability
 Performance
 Security

10. UML DIAGRAMS :
:
Data Server
End User
Browse File
Encrypt File
Send File
Upload
Response
Store Encrypted Data
asdfa
View Attackers
asdfa
View Owner Files
asdfa
View Attackers
asdfa
Search for both SSED and
K nearest neighbor search
View ‘N’ Ranked Data
Request data
Data Owner
Retrieve and store data
Usecase diagram:

11. Members
Methods
Methods
Members
Members
Methods
Members
Methods
Methods
Members
Browse File, Encrypt File ,
Upload File, View All, Exit,
View_owner_Files
Select File Name, Owner
Name, Owner File
Data Owner
View all Owner Files, View
Attacks,Store_Files,Authori
ze_Files,Authorize
users,Encrypted_Files
File ID, File Name, Owner
Name, Secret Key, User
Details, File Access Details,
View User Property,
Hackers, Exit
Data Server
Login, Register, Reset
User Name, Password
Login
Register, Reset
Name, Password, DOB,
Gender, Address, City,
Country, Email, Mobile
Register
Search File Based_
On_ SSED and K
nearest neighbor
search, Exit, Register,
and Login
Fname,rank,usernam
e,secret_key
Receiver
Class diagram :

12. SEQUENCE DIAGRAM
Browse & uploads file
File sent confirmation
Search response
Search response
Searching file for SPCHS and IBKEM
Gives secret key
Request secret key
View all data
provider files
Data provider End userData server
Checks file names
& aggregate key
Request SK
Save files
Download file
Check files name
and Secret key

13. •
Response
Response
Request Response
Data Owner Upload Enc Files Data Server
Response for both
the techniques such
as SSED and K
nearest neighbor
search with
searching ratio
End User
Request
Data by
keyword
Response data sent
information
Request
Find the Data Set
and upload data
Data flow diagram

14. Architecture Diagram
Data
Owner
End User
DATA SERVER
1) View user Details
2) View Attacker
Details
3) Unblock User
1) Registers & Login
1) Registers & Logins
Browse and enc
and Uploads files
with keywords, enc
keyword also.
1) Searches for files based on
Content’s keyword
2) Requests for Skey
3) Requests for downloading files
4) Find the file search ratio
5) Find all K Nearest Neighbor
search documents

15. Archiecture Dig Cont..

16. IMPLEMENTATION :
 Data provider
 Data Server
 END User

17. DATA PROVIDER
 In this module, the data provider uploads their data
in the Data server. For the security purpose the
data owner encrypts the data file and then store in
the server. The Data owner can have capable of
manipulating the encrypted data file.

18. DATA SERVER
 The Data server manages which is to provide data
storage service for the Data Owners. Data owners
encrypt their data files and store them in the Server for
sharing with data consumers. To access the shared data
files, data consumers download encrypted data files of
their interest from the Server and then Server will
decrypt them. The server will generate the aggregate
key if the end user requests multiple files at the same
time to access.

19. END USER
 In this module, the user can only access the data
file with the encrypted key word. The user can
search the file for both the methods such as SSED
and K nearest neighbor search. The user has to
register and then login from the Data server.

20. RESULTS:
DATA SERVER

21. DATA OWNER

22. DATA OWNER

23. OWNER DETAILS

24. TRANSACTIONS

25. DATA SERVER FILES

26. END USER

27. END USER

28. CONCLUSION
 To protect user privacy, various privacy-preserving
classification techniques have been proposed over
the past decade. The existing techniques are not
applicable to outsourced database environments
where the data resides in encrypted form on a third-
party server.
 This project proposed a novel privacy-preserving k-
NN classification protocol over encrypted data in
the server. This protocol protects the confidentiality
of the data, user’s input query, and hides the data
access patterns.