To support privacy-preserving video sharing, we have proposed a novel framework that is able to protect the video content privacy at the individual video clip level and prevent statistical inferences from video collections. To protect the video content privacy at the individual video clip level, we have developed an effective algorithm to automatically detect privacy-sensitive video objects and video events. To prevent the statistical inferences from video collections, we have developed a distributed framework for privacy-preserving classifier training, which is able to significantly reduce the costs of data transmission and reliably limit the privacy breaches by determining the optimal size of blurred test samples for classifier validation. Our experiments on a specific domain of  patient training and counseling videos  show convincing results.
['Jianping Fan', 'Hangzai Luo', 'Mohand-Said Hacid', 'Elisa Bertino']
A novel approach for privacy-preserving video sharing