Federated learning (FL) enables decentralized industrial-Internet-of-Things devices (also called clients) to share model parameters to build a joint model. Fair rewards, security of shared data, and transmission cost are the important factors that influence clients to participate in FL. Few existing works can solve these problems at the same time. Therefore, we propose a hierarchically fair and differentially private federated learning (HFDPFL), which regards the model itself as a reward to promote fairness. Reputation is used to measure the client's contribution to FL, and clients with high reputation will be rewarded with high accuracy models. In order to ensure the security of the shared data and reduce communication overhead, we implement differentially private gradient compression based on compressed sensing, which achieves differential privacy protection of gradients and improves communication efficiency. Extensive experiments are conducted to demonstrate the superiority of HFDPFL in terms of fairness, privacy preserving, and communication efficiency.

Watermarking schemes in plaintext domain usually suffer from high exposure risk of the cover signal. Data hiding in encrypted image (DHEI) can guarantee the security of the cover signal by embedding secret information into encrypted signal. Based on the above, we propose a robust watermarking scheme in encrypted domain based on integer lifting wavelet transform (LWT) and compressed sensing (CS). Firstly, the signal owner uses Kronecker CS progressive reconstruction and global random permutation (GRP) to realize the reserving room and encryption. Subsequently, the watermark is preprocessed by integer LWT, and compressed by CS. Then, the watermark is embedded by multiple embedding method (MEM) and most significant bits (MSBs) replacement in the cover signal. Experiments show that the proposed scheme can extract the watermark stably under a variety of attacks while the cover signal achieves satisfactory reconstruction.

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.

Compressed sensing (CS), a popular signal processing technique, can achieve compression and encryption simultaneously. Therefore, it has extension applications in various fields. However, CS is vulnerable to cryptographic attacks for its linear encoding process. To solve this problem, a permutation-diffusion structure is designed and embedded to the CS encoding process. In addition, it can increase the key space while compressing. Since the permutation-diffusion structure reduces the sparseness, superior recovery performance cannot be achieved. Therefore, the multi-prior regularization recovery strategy is designed to improve the recovery performance, where the multi-prior regularization term denotes l1 norm, total variation (TV) and low rank. The simulation results and analyses demonstrate that the proposed encoding scheme can resist cryptographic attacks, increase the key space while compressing, and achieve 1.54dB PSNR gain on average in comparison with the existing schemes.