Detection of Data Leakage by Using Fake Object Addition Technique

In the Networking field maintaining the secret is very important from hackers and some business field also it's very important, we have to send some sensitive data to some other third persons that time its normal to hacking and leaking the original data for that we have to use some technique to maintaining secret. A data distributor has given sensitive data to a set of supposedly trusted agents. If the data distributor has given set of data to the third parties that data is leaked and 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 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.