Multi-Level Privacy Preserving Scheme for Visual IoT Data Based on Compressive Sensing

With the rapid growth of IoT technology, Visual IoT (VIoT) plays a key role in areas like security surveillance and intelligent transportation, where large volumes of sensitive data are collected and transmitted. Many applications face limitations in computing and storage, such as challenges in managing traffic data. Recently, Compressed Sensing (CS) theory has been applied to improve data acquisition and processing efficiency. However, existing CS-based privacy methods focus mainly on protecting single privacy zones through key-based control and lack support for hierarchical protection across multiple sensitive regions. To address this, we propose a multi-level privacy-preserving scheme for VIoT data using CS, offering full, partial, and no access levels to meet varying security needs. We also implement watermarks to prevent key-sharing attacks among partially authorized users. Experimental results demonstrate that the scheme ensures data security while minimizing time and space overhead, making it suitable for resource-limited VIoT scenarios.