Author : Varsha Gaikwad 1
Date of Publication :7th April 2016
Abstract: In current years, for public advantage data need to be shared. Generally data is collected from distributed databases for e.g. in case of Health care and researches, data is collected from different providers and gathered in central network. In health care all information related to patient is present in central network which includes disease details, corresponding treatment and test details. In this paper, we consider the collaborative data publishing for anonymizing horizontally partitioned data at multiple data providers. Here, we are trying to come yet with some of the most basic yet unseen conclusions which will help both the government as well as the individual hospitals to identify the situation of their city people. By using anonymization technique the data is modified and then released to the public. This process is known as the privacy preservation data publishing. With the help of trusted third party data insertion of data, we are even considering “insider attack” and trying to make sure that the patient’s data is safe. This paper addresses this new thread, and makes several contributions. First, in order to make the patient’s information safe we are anonymizing the data using generalization and suppression algorithm. Second, displaying the results in the tabular form and graphical user interface form and implementing jFreeChart algorithm to display the graphical user interface data in pie chart and bar graph. Third inserting the data with the help of a third party who is trusted and no other person will be allowed to access the data. This is avoid “external” attacks.
Reference :
-
- Mingxuan Yuan, Lei Chen, Member, IEEE, Philip S. Yu, Fellow, IEEE, and Ting Yu-“Protecting Sensitive Labels in Social Network Data Anonymization”-IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 3, MARCH 2013.
- B. C. M. Fung, K.Wang, R. Chen, and P. S. Yu, “Privacy-preserving data publishing: A survey of recent developments,” ACM Comput.Surv., vol. 42, pp. 14:1–14:53, June 2010
- Karthikeyan.B,Manikandan. G,Vaithiyanathan. V,” A FUZZY BASED APPROACH FOR PRIVACY PRESERVING CLUSTERING”, Journal of Theoretical and Applied Information Technology, 2011, Vol. 32 No.2.
- http://www.tutorialspoint.com/jfreechart/jfreechart_b ar_chart.htm
- http://www.tutorialspoint.com/jfreechart/jfreechart_p ie_chart.htm