Detection of guilty agents

A data distributor has given sensitive data to a set of supposedly trusted agents. Sometimes data is leaked and found in unauthorized place e.g., on the web or on somebody's laptop. Data leakage happens every day when confidential business information such as customer or patient data, source code or design specifications, price lists, intellectual property and trade secrets, and forecasts and budgets in spreadsheets are leaked out. When these are leaked out it leaves the company unprotected and goes outside the jurisdiction of the corporation. This uncontrolled data leakage puts business in a vulnerable position. Once this data is no longer within the domain, then the company is at serious risk. When cybercriminals “cash out” or sell this data for profit it costs our organization money, damages the competitive advantage, brand, and reputation and destroys customer trust. To address this problem we develop a model for assessing the “guilt” of agents. The main focus of this project is the data allocation problem. It specifies how the distributor can “intelligently” give data to agents in order to improve the chances of detecting a guilty agent. By adding fake objects to distributed set, the distributor can find the guilt agent easily.