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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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10254 |
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| _version_ | 1846154528164937728 |
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| author | Piyush Nagasubramaniam Chen Wu Yuanyi Sun Neeraj Karamchandani Sencun Zhu Yongzhong He |
| author_facet | Piyush Nagasubramaniam Chen Wu Yuanyi Sun Neeraj Karamchandani Sencun Zhu Yongzhong He |
| author_sort | Piyush Nagasubramaniam |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3a87d072b2da49e7afb5e39024a04147 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3a87d072b2da49e7afb5e39024a041472024-11-26T17:48:03ZengMDPI AGApplied Sciences2076-34172024-11-0114221025410.3390/app142210254Privacy-Preserving Live Video Analytics for Drones via Edge ComputingPiyush Nagasubramaniam0Chen Wu1Yuanyi Sun2Neeraj Karamchandani3Sencun Zhu4Yongzhong He5Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USADepartment of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USAByteDance Inc., Beijing 100098, ChinaDepartment of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USADepartment of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USASchool of Computer, Beijing Jiaotong University, Beijing 100044, ChinaThe 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.https://www.mdpi.com/2076-3417/14/22/10254privacy-preservingvisual privacydrone video analyticsedge computingobject detection |
| spellingShingle | Piyush Nagasubramaniam Chen Wu Yuanyi Sun Neeraj Karamchandani Sencun Zhu Yongzhong He Privacy-Preserving Live Video Analytics for Drones via Edge Computing Applied Sciences privacy-preserving visual privacy drone video analytics edge computing object detection |
| title | Privacy-Preserving Live Video Analytics for Drones via Edge Computing |
| title_full | Privacy-Preserving Live Video Analytics for Drones via Edge Computing |
| title_fullStr | Privacy-Preserving Live Video Analytics for Drones via Edge Computing |
| title_full_unstemmed | Privacy-Preserving Live Video Analytics for Drones via Edge Computing |
| title_short | Privacy-Preserving Live Video Analytics for Drones via Edge Computing |
| title_sort | privacy preserving live video analytics for drones via edge computing |
| topic | privacy-preserving visual privacy drone video analytics edge computing object detection |
| url | https://www.mdpi.com/2076-3417/14/22/10254 |
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