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

How to Cite?

Mona Shah, Dr. Hiren D. Joshi, "Privacy Preserving Data Mining in a Shard Database: Architectural Aspect," SSRG International Journal of Computer Science and Engineering , vol. 2,  no. 3, pp. 26-29, 2015. Crossref,


 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.


Architecture, Data Mining, Distributed Database, Privacy Preserving.


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