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he pervasive operation of customer drones, or small-scale unmanned aerial vehicles (UAVs), has raised serious concerns about their privacy threats to the public. In recent years, privacy invasion events caused by customer drones have been frequently reported. Given such a fact, timely detection of invading drones has become an emerging task. Existing solutions using active radar, video or acoustic sensors are usually too costly (especially for individuals) or exhibit various constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. However, the state of the arts in this direction rely on line-of-sight (LOS) RF signals, which makes them only work under very constrained conditions. The support of more common scenarios, i.e., non-line-of-sight (NLOS), is still missing for low-cost solutions. In this paper, we propose a novel detection system for privacy invasion caused by customer drone. Our system is featured with accurate NLOS detection with low-cost hardware (under $50). By exploring and validating the relationship between drone motions and RF signal under the NLOS condition, we find that RF signatures of drones are somewhat “amplified” by multipaths in NLOS. Based on this observation, we design a two-step solution which first classifies received RSS measurements into LOS and NLOS categories; deep learning is then used to extract the signatures and ultimately detect the drones. Our experimental results show that LOS and NLOS signals can be identified at accuracy rates of 98.4% and 96% respectively. Our drone detection rate for NLOS condition is above 97% with a system implemented using Raspberry PI 3 B+.
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AliDrone: Enabling Trustworthy Proof-of-Alibi for Commercial Drone Compliance
Commercial use of Unmanned Aerial Vehicles (UAVs), or drones, promises to revolutionize the way in which consumers interact with retail services. However, the further adoption of UAVs has been significantly impeded by an overwhelming public outcry over the privacy implications of drone technology. While lawmakers have attempted to establish standards for drone use (e.g., No-Fly-Zones (NFZs)), at present a general technical mechanism for policy enforcement eludes state-of-the-art drones. In this work, we propose that Proof-of-Alibi (PoA) protocols should serve as the basis for enforcing drone privacy compliance. We design and implement AliDrone, a trustworthy PoA protocol that enables individual drones to prove their compliance with NFZs to a third party Auditor. AliDrone leverages trusted hardware to produce cryptographically-signed GPS readings within a secure enclave, preventing malicious drone operators from being able to forge geo-location information. AliDrone features an adaptive sampling algorithm that reacts to NFZ proximity in order to minimize the processing cost. Through laboratory benchmarks and field studies, we demonstrate that AliDrone provides strong assurance of geo-location while imposing an average of 1.5% overhead on CPU utilization and 0.3% of memory consumption. AliDrone thus enables the further proliferation of drone technology through the introduction of a trustworthy and accountable compliance mechanism.
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- PAR ID:
- 10085548
- Date Published:
- Journal Name:
- 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)
- Page Range / eLocation ID:
- 841 to 852
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDCS.2018.00086
