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Multi-Level Reversible Data Anonymization via Compressive Sensing and Data Hiding
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DOI：10.1109/TIFS.2020.3026467.png)
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摘要
En 中文
Recent advances in intelligent surveillance systems have enabled a new era of smart monitoring in a wide range of applications from health monitoring to homeland security. However, this boom in data gathering, analyzing and sharing brings in also significant privacy concerns. We propose a Compressive Sensing (CS) based data encryption that is capable of both obfuscating selected sensitive parts of documents and compressively sampling, hence encrypting both sensitive and non-sensitive parts of the document. The scheme uses a data hiding technique on CS-encrypted signal to preserve the one-time use obfuscation matrix. The proposed privacy-preserving approach offers a low-cost multi-tier encryption system that provides different levels of reconstruction quality for different classes of users, e.g., semi-authorized, full-authorized. As a case study, we develop a secure video surveillance system and analyze its performance.
Keyword:
Faces
Compressed sensing
Monitoring
Encryption
Privacy
Watermarking
Reversible privacy preservation
multi-level encryption
compressive sensing
video monitoring
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