Privacy-security oriented chaotic compressed sensing data collection in edge-assisted mobile crowd sensing

As a data-centric network, the Mobile Crowd Sensing (MCS) collects and uploads sensing data through intelligent terminal devices carried by workers. However, due to resource limitations, the confidentiality, integrity and communication cost issues of sensing data have not been well coordinated and resolved in the actual MCS data collection process. In this regard, this paper proposes an edge computing-assisted MCS Chaotic Compressed Sensing Secure Data Collection scheme (CCS-SDC), which supports the secure collection of sensing data and saves communication cost. In CCS-SDC, workers first use the encryption algorithm based on chaos theory to encrypt the collected sensing data, and then adopt the hash location algorithm based on chaos theory to calculate the corresponding hash verification code of the sensing data. After receiving the encrypted sensing data transmitted by the worker, the edge server recomputes the hash verification code of the encrypted sensing data and verifies the integrity of the data, which can locate the changed sensing task data to a certain extent. Then the sensing data is compressed and sampled based on the generated chaos measurement matrix to reduce the amount of data transmission and further enhance the confidentiality of the sensing data. In addition, the same hash positioning algorithm is used between the edge server and the sensing platform to protect data integrity. For the changed data located by integrity verification, in addition to choosing to let workers re-sense and submit, the sensing platform can also choose to discard the changed sensing data under appropriate circumstances, and still reconstruct and decrypt the remaining data through the proposed algorithm to obtain effective original sensing data. The experimental evaluation results on real data sets show that CCS-SDC achieves the best effects, not only achieving lower sensing data communication cost than other related schemes, but also better protecting the confidentiality and integrity of sensing data, which is very useful for resource-constrained MCS data collection scenarios.