Privacy-Preserving Live Video Analytics for Drones via Edge Computing

The use of lightweight drones has surged in recent years across both personal and commercial applications, necessitating the ability to conduct live video analytics on drones with limited computational resources. While edge computing offers a solution to the throughput bottleneck, it also opens the...

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Bibliographic Details
Published inApplied sciences Vol. 14; no. 22; p. 10254
Main Authors Nagasubramaniam, Piyush, Wu, Chen, Sun, Yuanyi, Karamchandani, Neeraj, Zhu, Sencun, He, Yongzhong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2024
Subjects
Classification
Computer vision
Data encryption
Deep learning
drone video analytics
Drones
Edge computing
Human subjects
Medical imaging equipment
Neural networks
object detection
Privacy
privacy-preserving
visual privacy
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Summary:The use of lightweight drones has surged in recent years across both personal and commercial applications, necessitating the ability to conduct live video analytics on drones with limited computational resources. While edge computing offers a solution to the throughput bottleneck, it also opens the door to potential privacy invasions by exposing sensitive visual data to risks. In this work, we present a lightweight, privacy-preserving framework designed for real-time video analytics. By integrating a novel split-model architecture tailored for distributed deep learning through edge computing, our approach strikes a balance between operational efficiency and privacy. We provide comprehensive evaluations on privacy, object detection, latency, bandwidth usage, and object-tracking performance for our proposed privacy-preserving model.
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content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app142210254