CI Compass
Angela Murillo
CI Compass is the NSF Center of Excellence for Navigating the Major Facilities (MFs) Data Lifecycle. MFs are the largest-scale scientific cyberinfrastructure (CI) efforts that the NSF supports and serve scientists, researchers, and the public by capturing and curating complex data from a variety of scientific instruments (from telescopes to sensors to research vessels). CI Compass brings together expertise from multiple disciplines to accelerate the MFs data lifecycle, to facilitate knowledge sharing and discovery, ensure the integrity and effectiveness of the CI upon which research and discovery depend.
Cyberwater - A sustainable data/model integration framework
Yao Liang
CyberWater2 is an open and sustainable data/model integration framework to facilitate collaboration across disciplines within the water domain. The framework aims to integrate heterogeneous data sources and enable two-way couplings among diverse computational models, eliminating the need for complex glue code. CyberWater2 also introduces a novel web service architecture, making it easily accessible via web browsers and adaptable AI-driven data agents to accommodate future modifications to external data sources.
Development of a Secure and Privacy-Preserving Workflow Architecture for Dynamic Data Sharing in Scientific Infrastructures
Xukai Zou
The project focuses on enhancing security and privacy measures on JetStream, a cloud platform funded by National Science Foundation, to meet strict requirements for handling sensitive data like protected health information (PHI). It establishes a robust cybersecurity architecture on JetStream to facilitate secure PHI sharing among users in collaborative research and education projects. This architecture ensures comprehensive protection for PHI and its workflows through user authentication, precise data access control, confidentiality, integrity, and traceability. Notably, this versatile architecture can be seamlessly implemented in various data and resource sharing environments.
Fully transparent, verifiable yet privacy-preserving electronic voting
Xukai Zou
The research project aims to develop a remote e-voting system that is transparent, verifiable, and privacy preserving. The system will enable voters to cast their ballots remotely while ensuring the integrity and accuracy of the voting process. It will allow voters to verify their individual plain vote and enable anyone to tally and verify the vote counts for every candidate. The system will be resistant to misbehavior and outside attacks, and any invalid votes or attacks can be detected with high probability. The project will also investigate receipt freeness, coercion-resistance, everlasting privacy, and quantum-safe e-voting solutions, and implement and deploy a secure remote electronic voting system in the real world.
Empowering Patient-Centric Health Information Exchange through Blockchain Technology
Yan Zhuang
This project introduces a blockchain solution for secure, patient-controlled health record access, addressing data security, privacy, and accessibility challenges in Health Information Exchange (HIE), aligning with Office of the National Coordinator for Health Information Technology's emphasis on patient-centric data ownership. A comprehensive simulation validates the model's effectiveness in enhancing data security and patient-centric care in HIE systems.
Revolutionizing Clinical Trial Management with Blockchain Technology
Yan Zhuang
This project aims to optimize clinical trial management systems (CTMS) by leveraging blockchain's transparency, traceability, immutability, and security to overcome data inconsistency and limited cross-site functionality in traditional CTMS. Our solution covers all trial stages, featuring a shareable master file system, rapid recruitment, secure data capture, reproducible analytics, and efficient payments. Using Quorum blockchain, the system securely handled 21.6 million transactions, and improved patient recruitment and trial management efficiency with the automated matching mechanism.
Resilient AI-based closed-loop automation security for 5G multi-access edge computing systems
Arjan Durresi
This study aims to design a framework to detect and mitigate fake and potentially threatening user communities in 5G social networks. By leveraging geo-location data, community trust dynamics, and AI-driven community detection algorithms, the framework identifies users who may pose harm by considering attributes that are challenging for malicious users to emulate.
Connecting Community-led Violence Prevention Efforts with Independent Real-time Gunshot Data
James Hill
This project aims to develop a low-cost acoustic gunshot detection network for civilian community intervention workers. The network uses off-the-shelf hardware and custom deep learning algorithms to recognize gunshots in real-time. Moreover, it incorporates a smartphone-based alert system to enable prompt responses from community intervention workers during violent incidents.
