If you are a vehicle manufacturer dealing with the risk of hackers taking control of car AI — this project developed quantum-resistant hardware and secure networking that protects data-in-transit and data-in-process. This ensures the car's intelligence remains trustworthy and secure from external attacks.
Quantum-Safe Secure Edge Computing for Trustworthy AI Deployment
Imagine a giant web of smart devices that can talk to each other without any risk of being hacked, even by future super-computers. It's like giving every device a digital vault and a secret code that changes instantly to keep data safe while it moves. This setup allows AI to run locally on devices instead of a central server, making it faster and more private.
What needed solving
Current edge computing systems are vulnerable to future quantum computing attacks and lack a transparent, auditable way to deploy AI across different hardware.
What was built
Preliminary designs for a Quantum-Resistant Secure Element (QR-SE) and a QR transceiver using KYBER and DILITHIUM algorithms.
Who needs this
Who can put this to work
If you are a payment provider dealing with the threat of future quantum computers breaking current encryption — this project developed a Quantum-Resistant Secure Element (QR-SE) using algorithms like KYBER and DILITHIUM. This keeps financial transactions secure against next-generation cyber threats.
If you are a factory operator dealing with complex AI deployment across many different machines — this project developed a drag-and-drop approach for deploying services via semantic programming. This allows for fast and reliable setup of AI processes across the factory floor.
Quick answers
What is the cost of implementing this technology?
Based on available project data, specific pricing or cost structures are not provided.
Can this be scaled to a large industrial environment?
Yes, the project focuses on a scalable cloud-edge continuum and will be tested through 5 use cases in sectors like automotive and telco.
How is the IP and licensing handled?
Based on available project data, the specific licensing terms are not mentioned, though it involves 13 SMEs maturing their technologies.
How does this integrate with existing AI systems?
It uses semantic-based interplay and graph-management to allow for a drag-and-drop deployment of services in a fast and reliable manner.
What is the timeline for market availability?
The project runs from 2024-06-01 to 2027-05-31, with technology maturing over this period.
Who built it
The consortium is heavily industry-driven, with 21 industry partners (64% ratio) and 13 SMEs, suggesting a strong focus on commercial viability. It spans 9 countries and combines the expertise of 8 universities and 4 research centers, providing a balanced mix of academic rigor and market application.
Contact the Consorzio Nazionale Interuniversitario per le Telecomunicazioni in Italy.
Talk to the team behind this work.
Contact us to connect with the 13 SMEs developing these quantum-resilient tools.