Relational Data Leakage Detection using Fake Object and Allocation Strategies

In today's world, there is need of many companies to outsource their sure business processes (e. g. marketing ,human resources) and related activities to a third party like their service suppliers. In many cases the service supplier desires access to the company's confidential information like customer data, bank details to hold out their services. And for most corporations the amount of sensitive data used by outsourcing providers continues to increase. So in today's condition data Leakage is a Worldwide Common Risks and Mistakes and preventing data leakage is a business-wide challenge. Thus we necessitate powerful technique that can detect such a dishonest. Traditionally, leakage detection is handled by watermarking, Watermarks can be very useful in some cases, but again, involve some modification of the original data. So in this paper, unobtrusive techniques are studied for detecting leakage of a set of objects or records. The model is developed for assessing the "guilt" of agents. The algorithms are present for distributing objects to agents, in a way that improves our chances of identifying a leaker. Finally, consider the option of adding "fake" objects to the distributed set. The major contribution in this system is to develop a guilt model using fake elimination concept