Privacy Preserving Data Mining in a Shard Database: Architectural Aspect

International Journal of Computer Science and Engineering
© 2015 by SSRG - IJCSE Journal
Volume 2 Issue 3
Year of Publication : 2015
Authors : Mona Shah, Dr. Hiren D. Joshi

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Citation:
MLA Style:

Mona Shah, Dr. Hiren D. Joshi, "Privacy Preserving Data Mining in a Shard Database: Architectural Aspect" SSRG International Journal of Computer Science and Engineering 2.3 (2015): 26-29.

APA Style:

Mona Shah, Dr. Hiren D. Joshi, (2015). Privacy Preserving Data Mining in a Shard Database: Architectural Aspect. SSRG International Journal of Computer Science and Engineering 2.3, 26-29.

Abstract:

 Data mining as defined generally is a journey of discovering the underlying unusual, unnoticed and undetected patterns of data .It is not merely an area of interest for the research community but it has a share of inquisitiveness also – inquisitiveness in terms of finding something new, unusual, expecting something of interest and need both. This slice of curiosity in data mining adds that extra care by being meticulous while handling such data. The concept of decentralization of data introduced the need of extra care to be taken. It features parameters like prevention of misuse of data, security of data and unambiguousness of data so that it yields more meaningful, interpretable and applicable results. Scattered data over a group of sites can be analysed to find the hidden patterns which can be useful for all the involved parties. This inculcates scope for areas like secured data mining viz. Privacy preserving data mining, collaborative data mining, cooperative data mining and a few more to name. This paper is an endeavour towards proposing framework for one the focal requirements of collaborative data mining: privacy preserving data mining. A number of solutions in term of algorithm have been suggested so far to achieve Privacy Preserving Data Miming (PPDM), each with its own dynamics. This paradigm aims towards achieving accuracy while maintaining vital level of confidentiality among the participants involved in group data mining. The solution proposed suggests the use of a randomisation in selection and the use of an intermediate party also. This paper also covers the comparison between a few similar solutions in the same neighbourhood.

References:

[1].Frawley, W., Piatetsky-Shapiro, G., Matheus, C., “Knowledge Discovery in Databases: An Overview”, AI Magazine, fall 1992, pp. 213-228, 1992
[2].Sherman S.M. Chow Jie-Han Lee Lakshminarayanan Subramanian, “Two-Party Computation Model for Privacy- Preserving Queries over Distributed Databases” Proceedings of the Network and Distributed System Security Symposium, NDSS 2009, San Diego, California, USA, 8th February - 11th February 2009. The Internet Society 2009
[3]. V. Kapoor,P. Poncelet,F. Trousset and M. Teisseire,” Privacy Preserving Sequential Pattern Mining in Distributed Databases”, CIKM ’06 Proceedings on 15th ACM international conference on Information and knowledge management. Pages 758 – 767
[4]. Sung Wook Baik, Jerzy Bala, Daewoong Rhee, ”An Agent Based Privacy Preserving Mining for Distributed Databases” CIS, volume 3314 of Lecture Notes in Computer Science,page 910-915. Springer, (2004) 
[5]. Murat Kantarcioglu_ Jaideep Vaidya, “An Architecture for Privacy-preserving Mining of Client Information”, CRPIT ’14 Proceedings of the IEEE international conference on Privacy, security and data mining – Volume 14 Pages 37-42. 
[6]. Omar Abdel Wahab, Moulay Omar Hachami, Arslan Zaffari, Mery Vivas, Gaby G. Dagher, DARM: A Privacypreserving Approach for Distributed Association Rules Mining on Horizontally-partitioned Data”, IDEAS’14 Proceedings of the 18th In international Database Engineering and Applications Symposium Pages 1-8
[7]. Alka Gangrade and Ravindra Patel,” Privacy Preserving Three-Layer Naïve Bayes Classifier for Vertically Partitioned Databases”, International Journal of Computer Applications © 2013 by IJCA Journal Volume 64 - Number 6 Year of Publication: 2013
[8]. Giovanni DiCrescenzo, “Privacy architecture for distributed data mining based on zero-knowledge collections of databases”. 
[9]Alex Gurevich, Ehud Gudes,” Privacy preserving Data Mining Algorithms without the use of Secure Computation or Perturbation”, 10th International Database Engineering and Applications Symposium (IDEAS'06) 
[10]. D.Karthikeswarant, V.M.Sudha2 , V.M.Suresh, A.Javed Sultan4,” A pattern based framework for privacy preservation through association rule mining”. IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012) March 30, 31, 2012Key Words:

Key Words:

Architecture, Data Mining, Distributed Database, Privacy Preserving.