Information disclosure, either through information sharing or covert channels, could lead to potential security vulnerabilities. Monitoring this manually is difficult, and an automated system that can monitor disclosure queries and reliably calculate the risk factor of different disclosure events can help to focus human intervention on the most damaging leaks. IAI and its collaborators from Penn State University will develop a comprehensive Risk Assessment of Disclosure via Automated Reasoning (RADAR) system, to dynamically analyze disclosure information on distributed and scalable systems, assess privacy leaks, and quantify the associated risk with respect to content and priority of the disclosure. Various information content and disclosure types, and with different types, formats, and security levels over distributed environments will be studied. RADAR provides automated support for identifying, categorizing, quantifying, and reasoning of information disclosure risks by considering cumulative risk due to disclosure events in a sequence of releases or attacks and the contextual information that enables domain-specific inference. It will leverage various theoretical models in statistical inference like probe selection and posterior estimation, and models in information theory like entropy approach, to obtain a set of analytical tools for information disclosure analysis. These tools will be integrated into a practical framework for agile, adaptive and general quantitative disclosure risk measurement for targeted applications. Methods will be designed for automatic categorization of information according to the type of anticipated risk that will be quantitatively assessed. Practical software tools will be developed to automatically compute disclosure risk for each disclosure event with near real-time implementation. The performance of RADAR will be demonstrated using realistic information disclosure applications over a distributed cloud environment.