International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 4 | Issue 4 | Year 2013 | Article Id. IJCTT-V4I4P190 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I4P190

Data Leakage Detection System for Diabetes Patients DB

Sonali Patil, Hemlata Bhole

Citation :

Sonali Patil, Hemlata Bhole, "Data Leakage Detection System for Diabetes Patients DB," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 4, pp. 893-897, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I4P190

Abstract

In both the industrial and defence area, a forceful need is rising for fast, yet secure, propagation of Information. We centre on field with one information source (sender) and many information sinks (recipients) where: (i) contribution is equally useful for the one who sends and for the one who receives data, (ii) disclosing a pooled information is beneficial to the addressee but adverse to the sender, and (iii) information sharing decisions of the sender are determined using imperfect monitoring of the (un)intended information leakage by the recipients. We study the following problem: A data distributor (a physician) has given sensitive data to a set of supposedly trusted agents (research labs). Some of the data are leaked and found in an unauthorized place (e.g., on the web or somebody’s laptop). The physician must assess the likelihood that the leaked data came from one or more research labs, as opposed to having been independently gathered by other means. We propose data allocation strategies (across the research labs) that improve the probability of identifying leakages. These methods do not depend on alterations of the released data (e.g., watermarks). In some cases, we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party.

Keywords

Fake object, Data leakage, Allocation strategy.

References

[1] Panagiotis Papadimitriou (Student Member, IEEE),and Hector Garcia (Molina, Member, IEEE), ‘Data Leakage Detection’, Department of Computer Science, Stanford University, Gates Hall 4A, Stanford, CA 94305-9040, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 23, NO. 1, JANUARY 2011
[2] Polisetty Sevani Kumari, Kavidi Venkata Mutyalu, ”Development of Data leakage Detection Using Data Allocation Strategies”, M.Tech, CSE, Sri Vasavi Engineering College, Pedatadepalli, Tadepalligudem W.G.Dt, A.P. India, INTERNATIONAL JOURNAL OF COMPUTER TRENDS AND TECHNOLOGY- VOLUME3 ISSUE4- 2012
[3] Rohit Pol, Vishwajeet Thakur, Ruturaj Bhise, Prof. Akash Kate, “Data leakage Detection “, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH AND APPLICATIONS (IJERA)
[4] Mr. Vaibhav Narawade (Associate Professor), Unnati Kavali (Student of P.V.P.P.C.O.E.), Tejal Abhang (Student of P.V.P.P.C.O.E.), ‘DATA ALLOCATION STRATEGIES IN DATA LEAKAGE DETECTION’, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH AND APPLICATIONS (IJERA) ISSN: 2248-9622, VOL. 2, ISSUE 2, MAR-APR 2012
[5] http://archive.ics.uci.edu/ml/machine-learning-databases/diabetes/
[6] Sridhar Gade, Kiran Kumar Munde, Department of CSE, DRK Institute of Science and Technology ,Krishnaiah.R.V. Principal, Department of CSE, DRK Institute of Science and Technology, Andhra Pradesh,"DATA ALLOCATION STRATEGIES FOR LEAKAGE DETECTION",IOSR JOURNAL OF COMPUTER ENGINEERING (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727 VOLUME 5, ISSUE 2 (SEP-OCT. 2012), PP 30-35
[7] The Complete Reference Java, Seventh Edition, Herbert Schildt, Tata McGraw-Hill Education(2006)
[8] Software Engineering A Practioner's Approach, Fifth Edition, Roger S. Pressman
[9] The Unified Modelling Language User Guide, Second Edition, Grady Booch, James Rumbaugh, Ivar Jacobson