A Secure and Efficient Data Transmission Method With Multilevel Concealment Function Based on Chaotic Compressive Sensing

As a result of the information explosion and the development of the technology of the Internet of Things (IoT), tons of data are collected and transmitted, some of which may concern private or confidential information. Sensors, as the building blocks of such processes, are commonly resource-limited. For instance, they rarely possess large storage space, abundant electricity reserves, or sufficient computing power. So, how to improve the efficiency of data acquisition and transmission without trimming security has attracted much attention from researchers. Recently, compressive sensing (CS) theory has been exploited to improve the efficiency of data collection and transmission, while there are some drawbacks to existing CS-based data collection and transmission schemes. For instance, many existing CS-based data transmission methods lack data encryption or privacy protection mechanisms, meaning that private or confidential information may be exposed to uncertainty if such data are captured during transmission. This article proposes a method of collecting and transmitting data securely and efficiently based on
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-tensor product (PTP) CS and secret key encryption mechanism. It could provide data security under the assumption that participants and data processing domains are trusted. And adversaries could not obtain useful information, even if they manage to capture data that are transmitted. Compared with most existing compressive-sensing-based methods, the proposed method addresses privacy protection issues by realizing multilevel critical information concealment. By reducing the scale of measurement matrices, it achieves lower computing and storage resource consumption. To transmit data more securely, it also provides an encryption mechanism with sufficient key space and high key sensitivity. Overall, the proposed method is secure, energy-efficient, and provides privacy protection functions, which makes it particularly suitable for IoT sensor nodes with restricted computing power and storage capacity.