Privacy-preserving video classification with convolutional neural networks

Sikha Pentyala, Rafael Dowsley, Martine de Cock

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 38th International Conference on Machine Learning
EditorsMarina Meila, Tong Zhang
Place of PublicationLondon UK
PublisherProceedings of Machine Learning Research (PMLR)
Number of pages13
Publication statusPublished - 2021
EventInternational Conference on Machine
Learning 2021
- Online, United States of America
Duration: 18 Jul 202124 Jul 2021
Conference number: 38th (Proceedings) (Website)


ConferenceInternational Conference on Machine
Learning 2021
Abbreviated titlePMLR 2021
Country/TerritoryUnited States of America
Internet address

Cite this