Obfuscated Location Disclosure for Remote ID Enabled Drones

• URL: http://arxiv.org/abs/2407.14256v1
• Date: Fri, 19 Jul 2024 12:35:49 GMT
• Title: Obfuscated Location Disclosure for Remote ID Enabled Drones
• Authors: Alessandro Brighente, Mauro Conti, Matthijs Schotsman, Savio Sciancalepore,
• Abstract summary: We propose Obfuscated Location disclOsure for RID-enabled drones (OLO-RID) Instead of 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.
• Score: 57.66235862432006
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Chasing the Intruder: A Reinforcement Learning Approach for Tracking Intruder Drones [0.08192907805418582]
  We propose a reinforcement learning based approach for identifying and tracking any intruder drone using a chaser drone. Our proposed solution uses computer vision techniques interleaved with the policy learning framework of reinforcement learning. The results show that the reinforcement learning based policy converges to identify and track the intruder drone.
  arXiv  Detail & Related papers  (2023-09-10T16:31:40Z)
• Sound-based drone fault classification using multitask learning [7.726132010393797]
  This paper proposes a sound-based deep neural network (DNN) fault classifier and drone sound dataset. The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber. Using the acquired dataset, we train a classifier, 1DCNN-ResNet, that classifies the types of mechanical faults and their locations from short-time input waveforms.
  arXiv  Detail & Related papers  (2023-04-23T17:55:40Z)
• Unauthorized Drone Detection: Experiments and Prototypes [0.8294692832460543]
  We present a novel encryption-based drone detection scheme that uses a two-stage verification of the drone's received signal strength indicator ( RSSI) and the encryption key generated from the drone's position coordinates.
  arXiv  Detail & Related papers  (2022-12-02T20:43:29Z)
• TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos [57.92385818430939]
  Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. Existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices. We propose a simple yet effective framework, itTransVisDrone, that provides an end-to-end solution with higher computational efficiency.
  arXiv  Detail & Related papers  (2022-10-16T03:05:13Z)
• A dataset for multi-sensor drone detection [67.75999072448555]
  The use of small and remotely controlled unmanned aerial vehicles (UAVs) has increased in recent years. Most studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the dataset. We contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files.
  arXiv  Detail & Related papers  (2021-11-02T20:52:03Z)
• A simple vision-based navigation and control strategy for autonomous drone racing [0.0]
  We present a control system that allows a drone to fly autonomously through a series of gates marked with ArUco tags. A simple and low-cost DJI Tello EDU quad-rotor platform was used. We have created a Python application that enables the communication with the drone over WiFi, realises drone positioning based on visual feedback, and generates control.
  arXiv  Detail & Related papers  (2021-04-20T08:02:02Z)
• Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
  This paper attempts to address the problem of drones detection from other flying drones variations. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging. To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
  arXiv  Detail & Related papers  (2021-03-31T17:43:31Z)
• Mind the GAP: Security & Privacy Risks of Contact Tracing Apps [75.7995398006171]
  Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy. We demonstrate that in real-world scenarios the GAP design is vulnerable to (i) profiling and possibly de-anonymizing persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts.
  arXiv  Detail & Related papers  (2020-06-10T16:05:05Z)
• Privacy-Protection Drone Patrol System based on Face Anonymization [1.20050068684031]
  This work proposes face-anonymizing drone patrol system. One person's face in a video is transformed into a different face with facial components maintained. Our system is evaluated with a customized drone consisting of a high-resolution camera, a companion computer, and a drone control computer.
  arXiv  Detail & Related papers  (2020-05-29T05:14:18Z)
• Detection and Tracking Meet Drones Challenge [131.31749447313197]
  This paper presents a review of object detection and tracking datasets and benchmarks, and discusses the challenges of collecting large-scale drone-based object detection and tracking datasets with manual annotations. We describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions.
  arXiv  Detail & Related papers  (2020-01-16T00:11:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.