Privacy-Preserving Drone Navigation Through Homomorphic Encryption for Collision Avoidance

TL;DR

This paper introduces a homomorphic encryption-based method enabling drones to collaboratively avoid collisions without revealing their flight paths, balancing privacy and safety efficiently.

Contribution

It presents a novel homomorphic encryption technique for collision avoidance that preserves privacy and outperforms previous garbled circuit methods in speed and communication.

Findings

Faster collision detection compared to garbled circuits

Reduced network communication requirements

Effective privacy preservation against attacks

Abstract

As drones increasingly deliver packages in neighborhoods, concerns about collisions arise. One solution is to share flight paths within a specific zip code, but this compromises business privacy by revealing delivery routes. For example, it could disclose which stores send packages to certain addresses. To avoid exposing path information, we propose using homomorphic encryption-based comparison to compute path intersections. This allows drones to identify potential collisions without revealing path and destination details, allowing them to adjust altitude to avoid crashes. We implemented and tested our approach on resource-limited virtual machines to mimic the computational power of drones. Our results demonstrate that our method is significantly faster and requires less network communication compared to a garbled circuit-based approach. We also provide a security analysis of the approach against potential attacks.