MRI: Development of An Instrument for Secure Cyber Physical Systems Analytics

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This project aims to develop a secure and scalable instrument that can efficiently perform big data analytics on data collected from Internet of Things (IoT) devices while providing tools for preserving data security and privacy. IoT devices are monitoring and controlling systems that interact with the physical world by collecting, processing and transmitting data using the internet. IoT devices include home automation systems, smart grid, transportation systems, medical devices, building controls, manufacturing and industrial control systems. With the increase in deployment of IoT devices, the amount of data generated by these devices also increases. There is thus a need for large-scale, and secure data processing systems to process and extract information for efficient and impactful decision-making. The issue of trustworthiness in computation and data security arises when the IoT data contains sensitive information. For example, data collected using the home health devices such as a wireless blood-pressure monitor may need to be analyzed and correlated with other information. In these cases, data owners may need to protect their data and demand guaranties about data security and integrity. Development of this instrument will enable new research projects that require efficient and secure processing of IoT data. Consequently, this may allow the creation of novel IoT data processing tools and services which are not feasible today due to security and privacy concerns. The proposed instrument will integrate two important components in a novel and unique way. First, an IoT data gathering component that can collect various data from IoT devices including the industrial IoT (IIoT) devices, will be developed. This component will create the necessary data gathering part of the instrument. It will allow researchers to adjust data collection frequency and granularity to enable different types of data collection activities for various research projects. The second component of the instrument will be the secure data analytics layer that can process the potentially sensitive IoT data including the network packets, sensor data, etc. As part of this component, recently developed secure data processing techniques which leverage trusted execution environments (TEEs) will be implemented. In addition, these TEE-based techniques will be tailored for different types of IoT data to increase their efficiency and limit any sensitive data leakage. During this project, these components will be integrated using custom developed software which will be open sourced at the final stage. Furthermore, these components will be integrated to test various research tools with respect to scalability, security and data privacy. The developed instrument will be made available to our collaborators and larger scientific community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.