A distributor has given valuable data to a set of third parties or we can call them as our agents. If the data distributed to third parties is found in a public / private environment then finding the guilty party is a nontrivial task to distributor. Earlier the leakage of data was identified by water marking and pertubation technique which requires modification of data. Both these techniques alters the original content. Thereby the originality of data may be lost. 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 analyze a guilt model that detects the agents using allocation strategies without modifying the original data.. The idea is to distribute the date intelligently to agents based on sample data request and explicit data request in order to improve the chance of detecting the guilty agents. The algorithm 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.
Sensitive Data; Fake Objects; Data Allocation Strategies
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