group
What campus are you from?
Daytona Beach
Authors' Class Standing
Luke Mauro, Senior Kanchon Gharami, Graduate Student
Lead Presenter's Name
Luke Mauro
Faculty Mentor Name
Shafika Showkat Moni
Abstract
Autonomous drones have many applications including reconnaissance, surveillance, delivery, agriculture, and cinematography. However, security and privacy remain critical challenges due to vulnerabilities in wireless communication and the need to ensure airspace compliance as drones traverse multiple flight zones. While necessary for safety, these authentication techniques can compromise privacy or suffer from single points of failure. This paper proposes a decentralized, privacy-preserving authentication system using blockchain technology to authenticate drones across multiple flight zones while preserving the anonymity of the drone and its flight path. The system assigns drones unique PseudoIDs for each flight zone, which are stored and verified on a blockchain and may be used for authentication. The PseudoID, unrelated to a pre-registered RealID, prevents tracking while still ensuring authenticity. Additionally, the decentralized nature of the blockchain mitigates single points of failure. This work aims to demonstrate the proposed system, estimate performance, and show its applicability to drone networks.
Did this research project receive funding support from the Office of Undergraduate Research.
No
Privacy Preserving Drone Authentication Framework with Blockchain
Autonomous drones have many applications including reconnaissance, surveillance, delivery, agriculture, and cinematography. However, security and privacy remain critical challenges due to vulnerabilities in wireless communication and the need to ensure airspace compliance as drones traverse multiple flight zones. While necessary for safety, these authentication techniques can compromise privacy or suffer from single points of failure. This paper proposes a decentralized, privacy-preserving authentication system using blockchain technology to authenticate drones across multiple flight zones while preserving the anonymity of the drone and its flight path. The system assigns drones unique PseudoIDs for each flight zone, which are stored and verified on a blockchain and may be used for authentication. The PseudoID, unrelated to a pre-registered RealID, prevents tracking while still ensuring authenticity. Additionally, the decentralized nature of the blockchain mitigates single points of failure. This work aims to demonstrate the proposed system, estimate performance, and show its applicability to drone networks.