Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing

TL;DR

This paper introduces a reversible, multi-level encryption method combined with compressive sensing for privacy-preserving video monitoring, enabling efficient data protection with multiple de-identification levels.

Contribution

It proposes a novel reversible privacy-preserving scheme that supports multiple de-identification levels and combines encryption with compressive sensing for efficient data handling.

Findings

Effective face anonymization demonstrated through reconstruction quality.

Supports multiple privacy levels for de-identification.

Efficient data acquisition, encryption, and data hiding achieved.

Abstract

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.