TY - GEN
T1 - PriSE
T2 - 6th IEEE International Conference on Collaboration and Internet Computing, CIC 2020
AU - Fitwi, Alem
AU - Chen, Yu
AU - Zhu, Sencun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - With a myriad of edge cameras deployed in urban areas, many people are seriously concerned about the invasion of their privacy. The edge computing paradigm allows enforcing privacy-preserving measures at the point where the video frames are created. However, the resource constraints at the network edge make existing compute-intensive privacy-preserving solutions unaffordable. In this paper, we propose slenderized and efficient methods for Privacy-preserving Surveillance as an Edge service (PriSE) after investigating a spectrum of image-processing, image scrambling, and deep learning (DL) based mechanisms. At the edge cameras, the PriSE introduces an efficient and lightweight Reversible Chaotic Masking (ReCAM) scheme preceded by a simple foreground object detector. The scrambling scheme prevents an interception attack by ensuring end-to-end privacy. The simplified motion detector helps save bandwidth, processing time, and storage by discarding those frames that contain no foreground objects. On a fog/cloud server, the scrambling scheme is coupled with a robust window-detector to prevent peeping via windows and a multi-tasked convolutional neural network (MTCNN) based face-detector for the purpose of de-identification. The extensive experimental studies and comparative analysis show that the PriSE is able to efficiently detect foreground objects and scramble frames at the edge cameras, and detect and denature window and face objects at a fog/cloud server to ensure end-to-end communication privacy and anonymity, respectively. This is done just before the frames are sent to the viewing stations.
AB - With a myriad of edge cameras deployed in urban areas, many people are seriously concerned about the invasion of their privacy. The edge computing paradigm allows enforcing privacy-preserving measures at the point where the video frames are created. However, the resource constraints at the network edge make existing compute-intensive privacy-preserving solutions unaffordable. In this paper, we propose slenderized and efficient methods for Privacy-preserving Surveillance as an Edge service (PriSE) after investigating a spectrum of image-processing, image scrambling, and deep learning (DL) based mechanisms. At the edge cameras, the PriSE introduces an efficient and lightweight Reversible Chaotic Masking (ReCAM) scheme preceded by a simple foreground object detector. The scrambling scheme prevents an interception attack by ensuring end-to-end privacy. The simplified motion detector helps save bandwidth, processing time, and storage by discarding those frames that contain no foreground objects. On a fog/cloud server, the scrambling scheme is coupled with a robust window-detector to prevent peeping via windows and a multi-tasked convolutional neural network (MTCNN) based face-detector for the purpose of de-identification. The extensive experimental studies and comparative analysis show that the PriSE is able to efficiently detect foreground objects and scramble frames at the edge cameras, and detect and denature window and face objects at a fog/cloud server to ensure end-to-end communication privacy and anonymity, respectively. This is done just before the frames are sent to the viewing stations.
UR - http://www.scopus.com/inward/record.url?scp=85100763247&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100763247&partnerID=8YFLogxK
U2 - 10.1109/CIC50333.2020.00024
DO - 10.1109/CIC50333.2020.00024
M3 - Conference contribution
AN - SCOPUS:85100763247
T3 - Proceedings - 2020 IEEE 6th International Conference on Collaboration and Internet Computing, CIC 2020
SP - 125
EP - 134
BT - Proceedings - 2020 IEEE 6th International Conference on Collaboration and Internet Computing, CIC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 December 2020 through 3 December 2020
ER -