SECURE MINING OF ASSOCITION RULUES OVER HORIZONTALLY PARTIONED DATA IN DATA MINING

Dr. M.Mohan Rao; M.Laxmaiah

SECURE MINING OF ASSOCITION RULUES OVER HORIZONTALLY PARTIONED DATA IN DATA MINING

Dr. M.Mohan Rao * §

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M.Laxmaiah

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§ Tirumala Engg College

SECURE MINING OF ASSOCITION RULUES OVER HORIZONTALLY PARTIONED DATA IN DATA MINING

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Abstract

Data mining can extract important knowledge from large data collection. Sometimes these large collections are split among various parties, which can share the data. For example, insurance companies share the data from medical hospitals. Privacy concerns may prevent the parties from directly sharing the data. Here this project addresses secure mining of association rules over horizontally partitioned data. The existing work provides security by using some techniques like Randomization, Secure Multi Party computation, etc., The drawbacks in the existing work are inaccuracy, inefficiency and lacking of security. To overcome the drawbacks of existing system we proposed a new method by using Commutative Encryption tool, Randomization and Secure multi party computation. This work also provides security for multiple numbers of sites. Examples include knowledge discovery among intelligence services of different countries and collaboration among corporations without revealing trade secrets.

References

11 Cites in Article

Reference Format

Dimitris Achlioptas (2004). Random Matrices in Data Analysis.
J Vaidya,C Clifton (2002). Prviacy Preserving Association Rule Mining in Vertically Partitioned Data.
Jie Wang,Jun Zhang,Shuting Xu,Weijun Zhong (2007). A novel data distortion approach via selective SSVD for privacy protection.
Rakesh Agrawal,Ramakrishnan Srikant (2000). Privacy-preserving data mining.
A Evfimievski,R Srikant,R Agrawal,J Gehrke (2002). Pr i vacy Preserving Mining of Association Rules.
R Rivest,A Shamir,L Adleman (1978). A Method for Obtaining Digital Signatures and Public-Key Cryptosystems.
S Pohlig,M Hellman (1978). An Improved Algorithm for Computing Logarithms over GF(p) and Its Cryptographic Significance.
O Goldreich (2003). Encryption Schemes.
Y Lindell,B Pinkas (2000). Privacy Preserving Data Mining.
I Ioannidis,A Grama (2003). An Efficient Protocol for Yao's Millionaires'Problem.
M Reiter,A Rubin Crowds: Anonymity for Web Transactions.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. M.Mohan Rao, M.Laxmaiah. 1970. "SECURE MINING OF ASSOCITION RULUES OVER HORIZONTALLY PARTIONED DATA IN DATA MINING". Global Journal of Computer Science and Technology GJCST Volume 10 (GJCST Volume 10 Issue 14).

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GJCST Volume 10 Issue 14

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Submission ReceivedFebruary 7, 2010
Peer Review Double Blind
Handling Editor
Accepted March 2, 2010
Published March 15, 2010

Version of record

v1.2

Issue date

October 14, 2010

Language

English

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Article Matrices

Total Score: 147

Country: Unknown

Subject: Global Journal of Computer Science and Technology

Authors: Dr. M.Mohan Rao , M.Laxmaiah (PhD/Dr. count: 1)

View Count (all-time): 139

Total Views (Real + Logic): 5573

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Publish Date: 2010 03, Mon

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Total Views: 5.6K

Total Downloads: 272

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