ZoomP3: Privacy-Preserving Publishing of Online Video Conference Recordings.

The COVID-19 epidemic has made online video conferencing extremely popular throughout the world, with many schools, companies and government sectors using video conferencing applications (e.g., Zoom, Google Meet) in a daily basis. These applications also provide local or cloud recording services, which allow the replay or sharing of video conference recordings (VCRs) in a later time. Such convenience, however, can easily cause infringement of privacy as meeting participants’ personally identifiable information (e.g., face, name, voice) may be exposed to the public without their awareness or consent. While privacy regulation and training can help relieve the situation, efficient and effective tools are also highly desired to protect the privacysensitive users in the VCRs before their public releases. In this work, we propose the first Privacy-Preserving Publishing system (ZoomP3 ) that automatically processes video and audio information in VCRs for privacy protection. Besides leveraging and integrating multiple state-of-the-art computer vision and audio processing tools seamlessly into our system, a number of optimization algorithms are proposed to improve the scalability of the system, enabling it to protect the privacy of long video conferences. We have conducted various tests with short and long videos, and the results (with online demos) verified that ZoomP3 system is suitable for largescale use. It may be applied as an online service, e.g., by Zoom, or by large organizations such as universities, research institutes and government sectors.