UNIFI: A Protocol for Association Rule Mining InVertically Distributed Databases

M. Suresh Babu*, K.F.Bharati**
* PG Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapur, India.
** Assistant Professor, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapur, India.
Periodicity:June - August'2015
DOI : https://doi.org/10.26634/jcom.3.2.3544

Abstract

Data warehouses or databases may store large amount of data. In such databases, much processing power is needed for mining association rules. Therefore the solution used is a distributed system. In data mining, association rules are useful for analyzing and predicting customer behavior. They play an important part in shopping basket data analysis, product clustering, catalogue design and store layout. In this paper, the authors have used a protocol Unifying Lists of Locally Frequent Item sets (UNIFI) [1] for mining association rules in vertically partitioned data. In this Proposed system, the authors have aimed to implement the UNIFI protocol for Association Rule Mining in vertically distributed database. This protocol depends on the Fast Distributed Mining (FDM) algorithm like UNIFI-KC (Kantarcioglu and Clifton) in [6]. FDM is an unsecured version of the apriori algorithm.

Keywords

Frequent Itemsets, FDM ,Association Rule Mining, Privacy Preserving Data Mining

How to Cite this Article?

Babu, M.S., and Bharati, K.F. (2015). UNIFI: A Protocol for Association Rule Mining in Vertically Distributed Databases. i-manager’s Journal on Computer Science, 3(2), 1-9. https://doi.org/10.26634/jcom.3.2.3544

References

[1]. Tamir Tassa, (2014). “Secure Mining of Association Rules in Horizontally Distributed Databases”, IEEE Transactions on Knowledge and Data Engineering, Vol. 26(4), pp.970-983.
[2]. T. Tassa and D. Cohen, (2012). “Anonymization of Centralized and Distributed Social Networks by Sequential Clustering”, IEEE Transactions on Knowledge and Data Engineering, Vol. 25(2), pp.311-324.
[3]. T. Tassa and E. Gudes, (2012). “Secure Distributed Computation of Anonymized Views of Shared Databases”, ACM Transactions on Database Systems, Vol. 37 (2), pp.1-44.
[4]. Ben David, N. Nisan, and B. Pinkas, (2008). “Fair play MP - A system for secure multi-party computation”, th Proceedings of 15 ACM Conference on Computer and Communications Security, pp.257–266.
[5]. S. Zhong, Z. Yang, and R. N. Wright, (2005). “Privacy Enhancing K-Anonymization of Customer Data”, th Proceedings of 25 ACM Symposium on Principles of Database Systems, pp.139– 147.
[6]. M. Kantarcioglu and C. Clifton, (2004). “Privacy Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data”, IEEE Transactions on Knowledge and Data Engineering, Vol.16(4), pp.1026–1037.
[7]. Chris Clifton, Murat Kantarcioglu, Jaideep Vaidya, Xiaodong Lin, and Michael Y. Zhu, (2003). “Tools for Privacy Preserving Distributed Data Mining,” SIGKDD Explorations, Vol. 4(2), pp.1-7.
[8]. J. Vaidya and C. Clifton, (2002). “Privacy Preserving Association Rule Mining in Vertically Partitioned Data”, th Proceedings of the 8 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 639-644.
[9]. D. W. L. Cheung, J. Han, V. T. Y. ng, A.W.C. Fu. A, (1996). “Fast Distributed Algorithm for Mining Association th Rules”, Proceedings of 4 IEEE International Conference on Parallel and Distributed Information Systems, pp. 31- 42.
[10]. D. Beaver, S. Micali, and P. Rogaway, (1990). “The Round Complexity of Secure Protocols”, Proceedings of nd the 22 Annual ACM Symposium on Theory of Computing, pp. 503–513.
[11]. A. C. Yao, (1982). “Protocols for Secure rd Computations”, Proceedings of the 23 Annual IEEE Symposium on Foundations of Computer Science, pp. 160 – 164.
[12]. R. Agrawal and R. Srikant, (1994). “Fast Algorithms for th Mining Association Rules”, Proceedings of the 20 International Conference on Very Large Data Bases. pp. 487–499. http://www.vldb.org/dblp/db/conf/vldb/vldb94- 487.html
[13]. Y. Lindell and B. Pinkas, (2004).“Privacy Preserving Data Mining,” Advances in Cryptology – CRYPTO 2000, pp. 36–54. http://link.springer.de/link/service/series/0558/ bibs/1880/18800036.htm
[14] . I. Ioannidis and A. Grama, (2003). “An Efficient Protocol for Yao's Millionaires Problem”, Hawaii International Conference on System Sciences (HICSS-36).
[15] . C. Clifton, M. Kantarcioglu, and J. Vaidya, (2002). “Defining Privacy for Data Mining”, National Science Foundation Workshop on Next Generation Data Mining, H. Kargupta, A. Joshi, and K. Sivakumar, Eds., Baltimore, MD, pp. 126–133.
[16]. R. Srikant and R. Agrawal, (1995). “Mining Generalized Association Rules”, International Conference Very Large Data Bases, pp. 487-499.
[17]. J. Zhan, S. Matwin, and L. Chang (2005). “Privacy Preserving Collaborative Association Rule Mining”, Data and Applications Security, pp.153–165.
[18]. S. C. Pohlig and M. E. Hellman,(1978). “An Improved Algorithm for Computing Logarithms Over GF(p) and its Cryptographic Significance”, IEEE Transactions on Information Theory, Vol. 24(1), pp. 106–110.
[19] . Maheshkumar Ramrao Gangasagare and Rafik Juber Thekiya, (2015). “Survey on Secure Mining of Association Rules in Horizontally Distributed Databases”, International Journal of Advance Research in Computer Science and Management Studies, Vol.3(3), pp.138-145.
[20] . Kishori Pawar and Y. B. Gurav, (2014). “Overview of Privacy in Horizontally Distributed Databases ”, International Journal of Innovative Research in Advanced Engineering, Vol.1(4), pp. 82-87
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.