With the widespread adoption of edge computing technologies and the increasing prevalence of deep learning models in these environments, the security risks and privacy threats to models and data have grown more acute. Attackers can exploit various techniques to illegally obtain models or misuse data, leading to serious issues such as intellectual property infringement and privacy breaches. Existing model access control technologies primarily rely on traditional encryption and authentication methods; however, these approaches exhibit significant limitations in terms of flexibility and adaptability in dynamic environments. Although there have been advancements in model watermarking techniques for marking model ownership, they remain limited in their ability to proactively protect intellectual property and prevent unauthorized access. To address these challenges, we propose a novel model access control method tailored for edge computing environments. This method leverages image style as a licensing mechanism, embedding style recognition into the model's operational framework to enable intrinsic access control. Consequently, models deployed on edge platforms are designed to correctly infer only on license data with specific style, rendering them ineffective on any other data. By restricting the input data to the edge model, this approach not only prevents attackers from gaining unauthorized access to the model but also enhances the privacy of data on terminal devices. We conducted extensive experiments on benchmark datasets, including MNIST, CIFAR-10, and FACESCRUB, and the results demonstrate that our method effectively prevents unauthorized access to the model while maintaining accuracy. Additionally, the model shows strong resistance against attacks such as forged licenses and fine-tuning. These results underscore the method's usability, security, and robustness.

With the growing interconnectivity in Internet of Things (IoT) fields like smart home, smart city, and Internet of Vehicles (IoV), the need for secure cross-domain authentication is crucial. Existing methods face issues such as centralized control, high overhead, and lack of anonymity and traceability. To address these issues, we propose BAT-CA, a blockchain-based crossdomain authentication solution. BAT-CA utilizes a consortium blockchain for decentralized authentication, leveraging public key hash addresses for anonymity and traceability. This approach includes intra-domain and cross-domain authentication methods tailored for devices within domains and across domains. Intradomain authentication leverages Schnorr signatures to incorporate domain node endorsement proofs for secure and efficient authentication. For cross-domain authentication, a transaction token based on blockchain transaction structures is designed, combining distributed key generation and aggregated threshold signatures. Device real IDs are encrypted and stored on the blockchain, enabling anonymous device traceability through decrypted fragments collected from domain node voting. A series of experiments in physical environments validate the effectiveness and efficiency of the proposed approach.

Differentially Private Federated learning (DPFL) applies differential privacy (DP) techniques to preserve clients’ privacy in Federated Learning (FL). Existing methods based on Gaussian Mechanism require the operations of model updates clipping and noise injection, which lead to a serious degradation in model accuracies. Several improved methods are proposed to mitigate the accuracy degradation by decreasing the scale of the injected noise. Different from previous methods, we firstly propose to enhance the model robustness against the DP noise for the accuracy improvement. In this paper, we develop a novel FL scheme with improved model robustness, called FedIMR, which can provide the client-level DP guarantee while maintaining a high model accuracy. We find that the injected noise leads to the fluctuation of loss values in the local training, hindering the model convergence seriously. This motivates us to improve the model robustness for narrowing down the bias of model outputs caused by the noise. The model robustness is evaluated with the signal-to-noise ratio (SNR) of each layer’s outputs. Two techniques are proposed to improve the output SNR, including the logit vector normalization (LVN) and dynamic clipping threshold (DCT). Specifically, LVN normalizes the logit vertor to make the optimization algorithm keep increasing the model output, which is the signal item of the output SNR. DCT dynamically adjusts the clipping threshold to reduce the noise item of the output SNR. We also provide the privacy analysis and convergence results. Experiments are conducted over three famous datasets to evaluate the effectiveness of our method. Both the theoretical results and empirical experiments confirm that our FedIMR can achieve a better accuracy-privacy tradeoff than previous methods.

Edge intelligence can significantly enhance the real-time performance and robustness of multimedia retrieval. However, its privacy and intellectual property security face various challenges. In this paper, we propose a model training framework based on the gradient optimization concept, synchronously optimizing model parameters and model license. This approach ensures that the trained model only correctly retrieves information for inputs containing the correct license, actively protecting its intellectual property by restricting its usability. Additionally, we devise a data irreversibility standardization method based on random perturbation to safeguard the privacy of both data and license. We conduct extensive experiments in edge intelligence scenarios, and the results demonstrate that, compared to the state-of-the-art approaches in this field, our method achieves accuracy closer to the baseline model. The injected license are more covertly secured, and the anti-fine-tuning capability is improved by an average of more than 25.1%.

COVID-19 outbreaks rapidly around the world, which is the enemy faced by all humankind. Since COVID-19 is mainly spread through close personal contact, searching close-contacts is key to controlling this virus's spread. This paper designs COVID-19 Tracer, a novel low-cost passive system for searching COVID-19 patients' close-contacts. Utilizing ubiquitous Wi-Fi probe requests, COVID-19 Tracer can quickly determine whether a person stays in one small space with a COVID-19 patient in the same period. Furthermore, it seeks to find out a close-contact with a novel rang-free judgment algorithm for location similarity. Finally, extensive experiments conducted in a school office building show our system's good performance, and the accuracy in finding out close-contacts is more than 98%.