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Zero-Trust Data Fabric for Industrial Internet of Things

Description:

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Computing and Software OBJECTIVE: To develop a robust zero-trust data fabric for industrial internet of things addressing Air Force sustainment and other interests DESCRIPTION: Recent years have witnessed the rise of Industrial Internet of Things (IIoT), a newly emergent networking paradigm that connects pervasive sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. Furthermore, powered by interconnected devices in IIoT, industrial enterprises have entered a new age of “big data”, where the volume, velocity and variety of sensory data they manage are exploding at relatively high rates. Such big sensory data constitutes the largest-ever information source that covers almost every aspect of manufacturing, and this has fundamentally changed the ways that products are made and delivered. However, this big treasure trove of information has also posed great challenges on the design and development of IIoT. Currently, one major challenge confronting us is how to store and share the big sensory data in a secure and privacy-aware manner in order to facilitate complex computing and data analysis tasks. To address this challenge, there is a need to develop a zero-trust data fabric for IIoT. This environment should initialize Cloud Native Access Point technologies at the ATHENA hybrid cloud edge to fully integrate with current security advancements in our Operational Technology ecosystem. It should further functionally bring Zero Trust Architecture from outside the DOD boundary to current and future OT networks. In this infrastructure, enterprises’ sensory data will need to be encrypted and stored in a peer-to-peer distributed file system. Each enterprise will need to possess full control on its own data, and only the parties who get permission from this enterprise will need to access the raw data. Additionally, the developed data fabric in this scenario would need to support privacy-aware and auditable data indexing and query, with each enterprise in this infrastructure dynamically specifying and adjusting the privacy level of its respective data. PHASE I: FEASIBILITY DOCUMENTATION. For this Direct-to-Phase II topic, applicants must show feasibility by demonstrating the ability to i.) design data encryption and access control schemes, ii.) design an encryption scheme that enables each enterprise to encrypt its data in an efficient way, iii.) design a scheme that will support multi-key encryption so that the disclosure of a single key will not lead to any privacy leakage, which provides strong privacy protection in zero-trust environments, and iv.) design an access control scheme based upon each enterprise having full control of its own data. PHASE II: Create an environment initializing Cloud Native Access Point technologies at the ATHENA hybrid cloud edge to fully integrate with current security advancements in our Operational Technology ecosystem. Functionally bringing Zero Trust Architecture from outside the DOD boundary to current and future OT networks. Estimated requirement is $1.8M with potential for additional funds from AFSC beginning in mid FY23. PHASE III DUAL USE APPLICATIONS: The developed zero-trust data fabric is proliferated to multiple commercial applications. A successful infrastructure would be marketed to commercial manufacturing, aerospace industry, and other customers. Additional markets could include the smart homes, construction, and power industries. REFERENCES: 1. Chenglin Miao, Wenjun Jiang, Lu Su, Yaliang Li, Suxin Guo, Zhan Qin, Houping Xiao, Jing Gao, and Kui Ren, "Privacy-Preserving Truth Discovery in Crowd Sensing Systems", ACM Transactions on Sensor Networks (TOSN), Vol. 15, No. 1, 2019. ; 2. Chenglin Miao, Qi Li, Houping Xiao, Wenjun Jiang, Mengdi Huai, and Lu Su, "Towards Data Poisoning Attacks in Crowd Sensing Systems", the 19th ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Los Angeles, USA, June 2018. ; 3. Chenglin Miao, Lu Su, Wenjun Jiang, Yaliang Li, and Miaomiao Tian, "A Lightweight Privacy-Preserving Truth Discovery Framework for Mobile Crowd Sensing Systems", the 36th KEYWORDS: DATA FABRIC; INTERNET OF THINGS
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