Integrating federated learning (FL) with differential privacy (DP) into regional carbon allowances management and trading enables collaborative model training that protects both local data privacy and model security. However, the noise injection in DP degrades model accuracy, necessitating additional local training and increasing the overall computational overhead for enterprises. This overhead reduces the willingness of rational and self-interested participants to contribute to model training. To address this challenge, this study models the interaction between the regulator and enterprises as a Stackelberg game and proposes a scalable, bandwidth-efficient FL framework for regional carbon allowances management and trading. The framework adaptively determines the number of local training iterations based on enterprises’ privacy budgets and employs a submodular enterprise selection strategy for aggregation, which decreases redundant uploads and enhances global model quality. Furthermore, a deep reinforcement learning–based multi-objective differential evolution with bi-level optimization (DRL-MODE2) algorithm is developed to efficiently solve large-scale, high-dimensional Stackelberg games. Comprehensive experiments were conducted on both a carbon emission time-series dataset and the MNIST benchmark dataset under different enterprise scales, incentive mechanisms, and optimization algorithms. Experimental results demonstrate that the proposed framework incentivizes enterprise participation and improves model accuracy, without compromising privacy preservation and communication efficiency.

Federated learning (FL) is a framework for distributed privacy-preserving computation, which enables global model aggregation by receiving client-trained models without accessing clients’ raw data. Therefore, developing an efficient incentive mechanism to promote client participation within this secure framework is a primary challenge in this field. To address this, we consider an FL system with differential privacy (DP) and model the interactions between the server and clients as a two-stage Stackelberg game aimed at minimizing server operating costs and maximizing client rewards. Then, a quality-based adaptive differential evolution-2 algorithm (Q-ADE2) is proposed to approximately solve the game model, which involves optimizing the server’s reward allocation to clients and determining the clients’ privacy budgets. In this approach, the quality coefficient is determined by the effectiveness of the FL system, evaluated based on the improvement in model accuracy. Finally, the standard MNIST dataset is employed in three comparative experiments, demonstrating that the proposed algorithm exhibits strong solving performance and effectively incentivizes client participation in FL training.

Nowadays, several image-based smart services have been widely used in our daily lives, generating many digital images. Since smart devices outsource digital images to the cloud, researchers prefer to select some desired targets from the massive images within the cloud for analysis and improve smart services. Therefore, protective image retrieval on the cloud has attained maximum concentration for privacy-preserving purposes, and the availability assurance of images on the cloud is also a crucial link. Ensuring image security and availability in the cloud environment and precisely preserving retrieval accuracy is comes as a utility-security dilemma while few existing works have explicitly addressed it. Therefore, this paper proposes privacy-preserving image retrieval in the distributed environment based on the combination of image encryption for similarity search and secret image sharing. On the basis of them, we define two-stage encryption. The first-stage encryption algorithm is introduced by modifying Wolfram's reversible cellular automata-based image encryption, which can create a set of processing images to ensure image security and retrieval accuracy. Then, the second-stage encryption algorithm is put forward based on secret image sharing to improve image security and availability. The color histogram could be extracted from the encrypted images for similarity retrieval, and the shadows could be extracted for similar image recovery. Security analysis demonstrates that image privacy and query privacy could be well protected. Moreover, the proposed work achieves more efficient performance for similarity search and similar image recovery compared with some recent works and realizes a reasonable retrieval accuracy on encrypted images for similarity search.

With the development and innovation of new techniques for 5G, 5G networks can provide extremely large capacity, robust integrity, high bandwidth, and low latency for multimedia image sharing and storage. However, it will surely exacerbate the privacy problems intrinsic to image transformation. Due to the high security and reliability requirements for storing and sharing sensitive images in the 5G network environment, verifiable steganography-based secret image sharing (SIS) is attracting increasing attention. The verifiable capability is necessary to ensure the correct image reconstruction. From the literature, efficient cheating verification, lossless reconstruction, low reconstruct complexity, and high-quality stego images without pixel expansion are summarized as the primary goals of proposing an effective steganography-based SIS scheme. Compared with the traditional underlying techniques for SIS, cellular automata (CA) and matrix projection have more strengths as well as some weaknesses. In this paper, we perform a complimentary of these two techniques to propose a verifiable secret image sharing scheme, where CA is used to enhance the security of the secret image, and matrix projection is used to generate shadows with a smaller size. From the steganography perspective, instead of the traditional least significant bits replacement method, matrix encoding is used in this paper to improve the embedding efficiency and stego image quality. Therefore, we can simultaneously achieve the above goals and achieve proactive and dynamic features based on matrix projection. Such features can make the proposed SIS scheme more applicable to flexible 5G networks. Finally, the security analysis illustrates that our scheme can effectively resist the collusion attack and detect the shadow tampering over the persistent adversary. The analyses for performance and comparative demonstrate that our scheme is a better performer among the recent schemes with the perspective of functionality, visual quality, embedding ratio, and computational efficiency. Therefore, our scheme further strengthens security for the images in 5G networks.