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2021-01-28
Goswami, U., Wang, K., Nguyen, G., Lagesse, B..  2020.  Privacy-Preserving Mobile Video Sharing using Fully Homomorphic Encryption. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). :1—3.

Increased availability of mobile cameras has led to more opportunities for people to record videos of significantly more of their lives. Many times people want to share these videos, but only to certain people who were co-present. Since the videos may be of a large event where the attendees are not necessarily known, we need a method for proving co-presence without revealing information before co-presence is proven. In this demonstration, we present a privacy-preserving method for comparing the similarity of two videos without revealing the contents of either video. This technique leverages the Similarity of Simultaneous Observation technique for detecting hidden webcams and modifies the existing algorithms so that they are computationally feasible to run under fully homomorphic encryption scheme on modern mobile devices. The demonstration will consist of a variety of devices preloaded with our software. We will demonstrate the video sharing software performing comparisons in real time. We will also make the software available to Android devices via a QR code so that participants can record and exchange their own videos.

2017-03-08
Xu, W., Cheung, S. c S., Soares, N..  2015.  Affect-preserving privacy protection of video. 2015 IEEE International Conference on Image Processing (ICIP). :158–162.

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. At the same time, there is an increasing need to share such video data across a wide spectrum of stakeholders including professionals, therapists and families facing similar challenges. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this paper, we propose a method of manipulating facial expression and body shape to conceal the identity of individuals while preserving the underlying affect states. The experiment results demonstrate the effectiveness of our method.