Abstrato

Data Leakage Identification and Blocking Fake Agents Using Pattern Discovery Algorithm

Karthik.R, Ramkumar.S, Sundaram.K

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. If the watermarked copy is found at some unauthorized site then distributor can claim his ownership. To overcome the disadvantages of using watermark, data allocation strategies are used to improve the probability of identifying guilty third parties. In this project, we implement and analyse a guilt model that detects the agents using a protocol. The guilty agent is one who leaks a portion of distributed data. The idea is to distribute the data intelligently to agents based on 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. Our goal is to detect when the distributor’s sensitive data have been leaked by agents, and if possible to identify the agent that leaked the data.