Privacy-Preserving Drone Navigation Through Homomorphic Encryption for Collision Avoidance

Fuente: arXiv

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Autori principali:	Luedeman, Allan, Baum, Nicholas, Quijano, Andrew, Akkaya, Kemal
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Emerging Technologies
Accesso online:	Acceder al recurso
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author	Luedeman, Allan Baum, Nicholas Quijano, Andrew Akkaya, Kemal
author_facet	Luedeman, Allan Baum, Nicholas Quijano, Andrew Akkaya, Kemal
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2507_14713
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Privacy-Preserving Drone Navigation Through Homomorphic Encryption for Collision Avoidance Luedeman, Allan Baum, Nicholas Quijano, Andrew Akkaya, Kemal Emerging Technologies 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.
title	Privacy-Preserving Drone Navigation Through Homomorphic Encryption for Collision Avoidance
topic	Emerging Technologies
url	https://arxiv.org/abs/2507.14713

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