Data Leakage Detection
Ahirrao P.P.1, Rai S.S.2, Pathania B. R.3
1Miss. Ahirrao P.P., Department of Computer, SCSCOE, Rahuri. Ahmednagar (Maharashtra), India.
2Mr. Rai S. S., Department of Computer, SCSCOE, Rahuri. Ahmednagar (Maharashtra), India.
3Mr. Pathania B.R., Department of Computer, SCSCOE, Rahuri. Ahmednagar (Maharashtra), India.
Manuscript received on 20 March 2014 | Revised Manuscript received on 25 March 2014 | Manuscript published on 30 March 2014 | PP: 85-89 | Volume-3 Issue-1, March 2014 | Retrieval Number: A1005033114/2014©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). If the data distributed to third parties is found in a public/private domain then finding the guilty party is a nontrivial task to distributor. Traditionally, this leakage of data is handled by water marking technique which requires modification of data. To overcome the disadvantages of using watermark [2], data allocation strategies are used to improve the probability of identifying guilty third parties. In this project, we implement and analyze a guilt model that detects the agents using allocation strategies without modifying the original data. The guilty agent is one who leaks a portion of distributed data. The idea is to distribute the data intelligently to agents based on sample data request and explicit data request in order to improve the chance of detecting the guilty agents. The algorithms implemented using fake objects will improve the distributor chance of detecting guilty agents. It is observed that by minimizing the sum objective the chance of detecting guilty agents will increase. We also developed a framework for generating fake objects.
Keywords: Sensitive data, Fake objects, Data allocation strategies, Data leakage, Data privacy, Fake record.
Scope of the Article: Data Mining