| VISION | Through continuous research and sound development practices, we aim to implement a lightweight, privacy-preserving algorithm that protects data in AI applications in IoT-cloud environments against emerging security threats. AI and IoT are transforming industries by making devices smarter, more connected, and efficient, but the increased connectivity creates security challenges. IoT devices often lack the resources for traditional security measures. Therefore, this creates the need for lightweight solutions that protect data without compromising performance. |
| MISSION | This project aims to design and implement a lightweight privacy protection algorithm for AI applications in IoT-cloud environments. By leveraging output layer-based pseudo-homomorphic encryption and a model split algorithm, we seek to secure AI-driven IoT systems without compromising performance. Our solution will focus on enhancing privacy and security for resource-constrained IoT devices, balancing computational efficiency with robust protection. |
| TEAM | Liam McGuckin (Project Manager), Jack McMaken (Technical Leader), Sriram Ranganathan (Research Czar), Joseph Brocato |
| CLIENT | Dr. Honglu Jiang Dr. Xianglong Feng |
| SITE | n/a |