Multi-Level Reversible Data Anonymization via Compressive Sensing and Data Hiding

Recent advances in intelligent surveillance systems have enabled a new era of smart monitoring wide range applications from health to homeland security. However, this boom data gathering, analyzing and sharing brings also significant privacy concerns. We propose Compressive Sensing (CS) based encryption that is capable both obfuscating selected sensitive parts documents compressively sampling, hence encrypting non-sensitive the document. The scheme uses hiding technique on CS-encrypted signal preserve one-time use obfuscation matrix. proposed privacy-preserving approach offers low-cost multi-tier system provides different levels reconstruction quality for classes users, e.g., semi-authorized, full-authorized. As case study, we develop secure video analyze its performance.