Encrypted Video Search: Scalable, Modular, and Content-similar

Video-based services have become popular. Clients often outsource their videos to the cloud to relieve local maintenance. However, privacy has emerged as a major concern since many videos contain sensitive information. While retrieving (unencrypted) videos has been widely studied, encrypted multimedia retrieval receives rare attention, at best in a limited form of similarity searches on images. We initiate the study of scalable encrypted video search in which a client can search videos similar to an image query. Our modular framework abstracts intrinsic attributes of videos in semantics and visuals to capture their contents. We advocate two-step searches by incorporating lightweight searchable encryption techniques for pre-screening and an interactive approach for fine-grained search. We provide two instantiations - The basic one searches over semantic keywords, feature extraction, and locality-sensitive hash-based visual representations. The advanced one employs forward and backward private searchable encryption [CCS 2017] over deep hashing [CVPR 2020]. Our experimental results illustrate their practical performance over multiple real-world datasets.