  Many video classification applications require access to personal data,
thereby posing an invasive security risk to the users' privacy. We propose a
privacy-preserving implementation of single-frame method based video
classification with convolutional neural networks that allows a party to infer
a label from a video without necessitating the video owner to disclose their
video to other entities in an unencrypted manner. Similarly, our approach
removes the requirement of the classifier owner from revealing their model
parameters to outside entities in plaintext. To this end, we combine existing
Secure Multi-Party Computation (MPC) protocols for private image classification
with our novel MPC protocols for oblivious single-frame selection and secure
label aggregation across frames. The result is an end-to-end privacy-preserving
video classification pipeline. We evaluate our proposed solution in an
application for private human emotion recognition. Our results across a variety
of security settings, spanning honest and dishonest majority configurations of
the computing parties, and for both passive and active adversaries, demonstrate
that videos can be classified with state-of-the-art accuracy, and without
leaking sensitive user information.
