Obfuscated Location Disclosure for Remote ID Enabled Drones

TL;DR

OLO-RID tackles the privacy risks of continuous RID broadcasts by introducing a 3D, PIM-based location obfuscation coupled with encrypted location reports for authorized access. It extends RID to maintain regulatory compliance while protecting UAV trajectories, achieving $\epsilon$-differential privacy on time-correlated location disclosures and enabling safe, authorized decryption via ECIES. The approach is implemented on a Raspberry Pi 3B+ and a laptop, demonstrating sub-0.16 s RID message generation and minimal energy overhead, with three real-world use cases showing favorable privacy-utility tradeoffs. This work offers a practical path toward privacy-preserving RID ecosystems that preserve accountability and safety benefits without revealing precise UAV locations in everyday deployments.

Abstract

The Remote ID (RID) regulation recently introduced by several aviation authorities worldwide (including the US and EU) forces commercial drones to regularly (max. every second) broadcast plaintext messages on the wireless channel, providing information about the drone identifier and current location, among others. Although these regulations increase the accountability of drone operations and improve traffic management, they allow malicious users to track drones via the disclosed information, possibly leading to drone capture and severe privacy leaks. In this paper, we propose Obfuscated Location disclOsure for RID-enabled drones (OLO-RID), a solution modifying and extending the RID regulation while preserving drones' location privacy. Rather than disclosing the actual drone's location, drones equipped with OLO-RID disclose a differentially private obfuscated location in a mobile scenario. OLO-RID also extends RID messages with encrypted location information, accessible only by authorized entities and valuable to obtain the current drone's location in safety-critical use cases. We design, implement, and deploy OLO-RID on a Raspberry Pi 3 and release the code of our implementation as open-source. We also perform an extensive performance assessment of the runtime overhead of our solution in terms of processing, communication, memory, and energy consumption. We show that OLO-RID can generate RID messages on a constrained device in less than 0.16 s while also requiring a minimal energy toll on a relevant device (0.0236% of energy for a DJI Mini 2). We also evaluate the utility of the proposed approach in the context of three reference use cases involving the drones' location usage, demonstrating minimal performance degradation when trading off location privacy and utility for next-generation RID-compliant drone ecosystems.

Figures (24)

• Figure 1: Sequence diagram of the operations required for the Registration Phase (upper part) and Runtime Phase (lower part).
• Figure 2: Sequence diagram of the operations executed in Use Case #1 to register the NFZ (upper part) and handle a RID message on the observer (lower part).
• Figure 3: Sequence diagram of the operations executed in Use Case #2 to register charging stations (upper part) and handle a RID messages on the observers (lower part).
• Figure 4: Sequence diagram of the operations executed in Use Case #3 to find the nearest UAV providing services.
• Figure 5: Representation of the flight path used for the experiments in Sec. \ref{['subsec:experiment_1_overhead_baseline']} and \ref{['subsec:experiment_2_epsilon_delta']}, with top-down and side view.

Theorems & Definitions (4)

• Definition 1
• Definition 2
• Theorem 1
• proof