PatronuS: A System for Privacy-Preserving Cloud Video Surveillance

• Published in: IEEE journal on selected areas in communications Vol. 38; no. 6; pp. 1252 - 1261
• Main Authors: Du, Haohua, Chen, Linlin, Qian, Jianwei, Hou, Jiahui, Jung, Taeho, Li, Xiang-Yang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Distillation; Grounding; Privacy; Security; Security management; Surveillance; surveillance systems; Task analysis; Video surveillance; Visualization
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2020.2986665

Privacy has become one of the major concerns in cloud video surveillance. Privacy protection of the surveillance videos strive to protect users' privacy information without hampering regular security tasks of the surveillance, meanwhile retains the system's high accuracy and efficiency. The current state of the art in protecting the video privacy is mainly realized through Privacy Region Protection, which only protects the privacy regions while keeps the non-privacy regions visually intact so that processing in the cloud is still feasible. However, the problem of determining the privacy regions has been ignored and not properly addressed. In this paper, we propose a novel notion - concept graph , and with the aid of that, we develop our system - PatronuS to determine the privacy regions subject to satisfying both privacy and security requirements. We further propose an event distilling model and a privacy inference model to assist in determining specific privacy regions. And we evaluate PatronuS in real-world settings and demonstrate its efficiency in privacy protection without degrading system's surveillance functionality.