Privacy-Preserving Live Video Analytics for Drones via Edge Computing

被引：0

作者：

Nagasubramaniam, Piyush ^{[1
]}

Wu, Chen ^{[1
]}

Sun, Yuanyi ^{[2
]}

Karamchandani, Neeraj ^{[1
]}

Zhu, Sencun ^{[1
]}

He, Yongzhong ^{[3
]}

机构：

[1] Penn State Univ, Dept Comp Sci & Engn, University Pk, PA 16802 USA

[2] ByteDance Inc, Beijing 100098, Peoples R China

[3] Beijing Jiaotong Univ, Sch Comp, Beijing 100044, Peoples R China

来源：

APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 22期

关键词：

privacy-preserving; visual privacy; drone video analytics; edge computing; object detection;

D O I：

10.3390/app142210254

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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.

引用

页数：18

共 24 条

[1]

Bewley A, 2016, IEEE IMAGE PROC, P3464, DOI 10.1109/ICIP.2016.7533003

[2] Enhancing Network-edge Connectivity and Computation Security in Drone Video Analytics [J].

Esquivel Morel, Alicia ;

Kavzak Ufuktepe, Deniz ;

Ignatowicz, Robert ;

Riddle, Alexander ;

Qu, Chengyi ;

Calyam, Prasad ;

Palaniappan, Kannappan .

2020 IEEE APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP (AIPR): TRUSTED COMPUTING, PRIVACY, AND SECURING MULTIMEDIA, 2020,

[3]

.faa.gov, Drones by the Numbers

[4]

Goodfellow I.J., 2014, arXiv, DOI [DOI 10.1145/3422622, 10.48550/arXiv.1406.2661]

[5]

Howard A, 2019, Arxiv, DOI [arXiv:1905.02244, 10.48550/arXiv.1905.02244, DOI 10.48550/ARXIV.1905.02244]

[6] Securing SIFT: Privacy-Preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data [J].

Hu, Shengshan ;

Wang, Qian ;

Wang, Jingjun ;

Qin, Zhan ;

Ren, Kui .

IEEE TRANSACTIONS ON IMAGE PROCESSING, 2016, 25 (07) :3411-3425

[7]

Goodfellow IJ, 2015, Arxiv, DOI [arXiv:1412.6572, DOI 10.48550/ARXIV.1412.6572]

[8]

Juvekar C, 2018, PROCEEDINGS OF THE 27TH USENIX SECURITY SYMPOSIUM, P1651

[9] SSD: Single Shot MultiBox Detector [J].

Liu, Wei ;

Anguelov, Dragomir ;

Erhan, Dumitru ;

Szegedy, Christian ;

Reed, Scott ;

Fu, Cheng-Yang ;

Berg, Alexander C. .

COMPUTER VISION - ECCV 2016, PT I, 2016, 9905 :21-37

[10] Privacy-Preserving Object Detection for Medical Images With Faster R-CNN [J].

Liu, Yang ;

Ma, Zhuo ;

Liu, Ximeng ;

Ma, Siqi ;

Ren, Kui .

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2022, 17 :69-84

← 1 2 3 →