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Security and Privacy

The ease of data collection and storage has led to the evolution of big data driven research in much of the biomedical research context. Research applicable data is now available at dispersed locations. Unfortunately, biomedical researchers are challenged by the non-availability of light weight tools that facilitate the discovery of such dispersed data sources and allow for federated queries on them. We aim to establish a hardware and software infrastructure that will harness proven web technologies and algorithms in private data integration to enable secure releases of traditional and non-traditional data, federated queries distributed over multiple databases, and unified views of medical records.

Critical civil, business and military infrastructures are undergoing massive levels of automation, giving rise to large subsystems where machines (or "things") sense the environment, interpret them, and make decisions without human intervention. Introducing connectivity to this design, hence the Internet-of-Things (IoT), multiplies the operational advantage we can gain in terms of efficiency and cost. The primary objective of our endeavor is to learn baseline models of large-scale IoT systems that can capture the normal operational behavior of a system, trends in information flow, association of actions and triggers, and the propagation of influence. The research will further promote explorations of how computationally operable representations of IoT systems can be used to identify critical components and attack vectors, assess residual risk in a deployed system, design resilient systems for mission assurance, and provide real-time anomaly detection.