  Security monitoring via ubiquitous cameras and their more extended in
intelligent buildings stand to gain from advances in signal processing and
machine learning. While these innovative and ground-breaking applications can
be considered as a boon, at the same time they raise significant privacy
concerns. In fact, recent GDPR (General Data Protection Regulation) legislation
has highlighted and become an incentive for privacy-preserving solutions.
Typical privacy-preserving video monitoring schemes address these concerns by
either anonymizing the sensitive data. However, these approaches suffer from
some limitations, since they are usually non-reversible, do not provide
multiple levels of decryption and computationally costly. In this paper, we
provide a novel privacy-preserving method, which is reversible, supports
de-identification at multiple privacy levels, and can efficiently perform data
acquisition, encryption and data hiding by combining multi-level encryption
with compressive sensing. The effectiveness of the proposed approach in
protecting the identity of the users has been validated using the goodness of
reconstruction quality and strong anonymization of the faces.
