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.

中文翻译：

---

通过压缩感知和数据隐藏进行多级可逆数据匿名化


智能监控系统的最新进展开启了从健康监控到国土安全等广泛应用的智能监控新时代。然而，数据收集、分析和共享的蓬勃发展也带来了严重的隐私问题。我们提出了一种基于压缩感知（CS）的数据加密，它能够混淆文档中选定的敏感部分并进行压缩采样，从而加密文档的敏感和非敏感部分。该方案在 CS 加密信号上使用一种数据隐藏技术来保留一次性使用的混淆矩阵。所提出的隐私保护方法提供了一种低成本的多层加密系统，该系统为不同类别的用户提供不同级别的重建质量，例如半授权、完全授权。作为案例研究，我们开发了一个安全视频监控系统并分析了其性能。