Multilevel Privacy Preservation Scheme Based on Compressed Sensing

Although the extensive application of the Internet of Things brings great convenience, it raises the concern of privacy leakage in the processes of data acquisition, analyzing, and sharing as well. In this article, multilevel privacy protection via compressed sensing (CS) is proposed, which has the advantages of compressed sampling, protection of data privacy, and controllability of data access. At the data acquisition end, the CS technique suitable for a resource-constrained environment is employed to sample and encrypt signals with the assistance of discriminant component analysis. Then, the encrypted data will be transmitted to the cloud in time. On the cloud service, signals protected by CS rarely disclose private information to malicious attackers, and they will be accessed by two-class authorized entities. One is the semiauthorized user with low privilege who can only get the features from encrypted data for the subsequent inference; the other is the full-authorized user who is capable of reconstructing the original data. We demonstrate the scheme through two case studies of a face recognition system and a human activity recognition system and analyze its performance.