A Model for Traitor Detection with Appraising Approach

In today‟s technically empowered world it is a major task for distributors to prevent their data from false agents. Data distribution across trusted third party agents is complicated and always in the danger of misconfiscation by the users or agents. Loss of large volumes of shielded information has become regular headline event. Due to this reason data accessing in a secure way is became a hot topic of research and it became a challenging part to identifying leakages. In case of a leak, nothing can be done by the data distributor. Only possibility is forcing companies to reissue cards, notify customers and mitigate loss of goodwill from negative publicity. The existing methods sometimes fail to do the same. In this work, we develop an algorithm for distributing data to agents, in such a way that improves the chances of identifying a leakage. We consider adding “fake data” objects to the distributed original data which do not correspond to real entities but appear realistic to the agents. Here this type of fake objects worn as a type of watermark for the entire set, without modifying any individual data. If an agent was given one or more fake objects that were leaked, then the distributor can find the corrupted agent who leaked the data. We consider adding “fake data” objects to the distributed original data which are not equivalent to real entities but appear as a practical one to the agents. If an agent was given one or more fake objects that were leaked, then the distributor can identify the guilty agent. General Terms Data leakage, Security, fake identification, authentication et. al.