alexa Privacy Preserving Data Analysis Technique
ISSN ONLINE(2319-8753)PRINT(2347-6710)

International Journal of Innovative Research in Science, Engineering and Technology
Open Access

Like us on: https://twitter.com/ijirset_r
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Special Issue Article

Privacy Preserving Data Analysis Technique

G.Monika1, R.Saraswathi2, K.Sujitha3, Mrs.M.Varalakshmi4
  1. Student, Department of CSE, Manakula Vinayagar Institute of Technology, Puducherry, India
  2. Assistant Professor, Department of CSE, Manakula Vinayagar Institute of Technology, Puducherry, India
Related article at Pubmed, Scholar Google
 

Abstract

In many cases, competing parties who have private data may collaboratively conduct Fraud detection tasks to learn beneficial data models or analysis results. For example, different credit card companies may try to build better models for credit card fraud detection through Fraud detection tasks. Similarly, competing companies in the same industry may try to combine their sales data to build models that may predict the future sales. In many of these cases, the competing parties have different incentives. Although certain fraud detection techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether or not participating parties are truthful about their private input data. In other words, unless proper incentives are set, even current Fraud detection techniques cannot prevent participating parties from modifying their private inputs. This raises the question of how to design incentive compatible Fraud detection techniques that motivate participating parties to provide truthful input data. In this paper, we first develop key theorems, then base on these theorem, we analyze what types of Fraud detection tasks could be conducted in a way that telling the truth is the best choice for any participating party.

Share This Page

Additional Info

Loading
Loading Please wait..
Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords