Multi-server authentication technology has become more and more popular with the extensive applications of networks. Although it has brought great convenience to people’s life, security becomes a critical issue and attracts lots of attentions in both academia and industry. Over the past two decades, a series of multi-server authentication schemes without communication with the online registration center in each authentication phase using the self-certified public key cryptography have been proposed to enhance security. However, it may cause the single-point failure problem due to the centralized architecture. Besides, user revocation facility is not well resolved in these schemes. To the best of our knowledge, blockchain technology has lots of advantages, bringing a promising solution to the problems of single-point failure and user revocation compared with the traditional cryptography technologies. In this work, we apply the idea of blockchain technology to construct a privacy-awareness authentication scheme for the multi-server environment, which can achieve distributed registry and efficient revocation. Moreover, the proposed scheme not only provides multiple security requirements like mutual authentication, user anonymity and perfect forward secrecy, but also resists various kinds of malicious attacks. The security of the proposed scheme is proved by rigorous formal proof using the random oracle model. Compared with recently related schemes, the proposed scheme has better communication performance, which make it be very suitable for real-life applications.

Huge amounts of data from various sources are substantial to dependable distributed machine learning, especially for trustworthy federated learning (FL). However, existing FL methods are difficult to collect enough data for training the global model more accurately, especially in cross-device scenarios. In this paper, we propose a new federated generative adversarial network empowered by differential privacy and knowledge transfer named Fed-GAN, which can be used to address the problem of data shortage and prevent generator leakage from resource-constrained devices, as well as generate high-quality synthetic data while ensuring strict DP guarantees. Different from other generative model methods, our Fed-GAN framework can achieve efficient and secure generative model training and limited permission for resource-constrained devices to prevent them from leaking or misusing the generator. In addition, we propose a pHash-KT method for our Fed-GAN framework, which selects potentially high-quality data through the knowledge of each client for improving the utility of synthetic data. Our FedGAN framework satisfies ($\frac{2kJ\lambda}{\sigma^2}+\frac{log 1/\delta}{\lambda-1},\delta}$)-DP, and also has high resistance when number of adversaries is 10%-70% of the total number of clients. Extensive experiments demonstrate that our Fed-GAN framework not only generates high-quality synthetic data, but also provides strict DP guarantees, compared with other generative model methods. Our code is publicly available at https://github.com/daxx1/fed-gan

Wireless sensor network (WSN), as one of the core technology in the Industrial Internet of Things (IIoT) system, plays a critical role in collecting data for the monitoring areas. Security and privacy are essential to ensure the trustworthy completeness of data access and transmission for the WSN-based IIoT system. Recently, a series of excellent privacy-preserving authentication schemes for the WSN-based IIoT system have been proposed. There is, however, strong demand for further improvements in multiple registrations, user revocation, and forward secrecy. Targeted by these security issues, we propose an efficient privacy-preserving authentication scheme with revocability for multi-WSNs in the IIoT environment. In the proposed scheme, the user can access multiple different WSNs with the same identity and password through a single registration. Our scheme is provable secure under the random oracle model, and security analysis via practical circumstances shows that it is able to provide a variety of security requirements, such as user revocation, forward secrecy, etc. In addition, the comparison with related schemes indicates that the proposed one has fewer sensor nodes-based computations and overall communication overheads. It is, therefore, more suitable for the resource-constrained IIoT environment than existing approaches.

Edge computing provides cloud services at the edge of the network for Internet of Things (IoT) devices. It aims to address low latency of the network and alleviates data processing of the cloud. This “cloud-edge-device” paradigm brings convenience as well as challenges for location-privacy protection of the IoT. In the edge computing environment, the fixed edge equipment supplies computing services for adjacent IoT devices. Therefore, edge computing suffers location leakage as the connection and authentication records imply the location of IoT devices. This article focuses on the location awareness in the edge computing environment. We adopt the “deniability” of authentication to prevent location leakage when IoT devices connect to the edge nodes. In our solution, an efficient deniable authentication based on a two-user ring signature is constructed. The robustness of authentication makes the fixed edge equipment accept the legal end devices. Besides, the deniability of authentication cannot convince any third party that the fact of this authentication occurred as communication transcript is no longer an evidence for this connection. Therefore, it handles the inherent location risk in edge computing. Compared to efficient deniable authentications, our protocol saves 10.728% and 14.696% computational cost, respectively.

Cloud storage technology is constantly evolving, resulting in a significant amount of duplicate data being stored in the cloud, particularly multimedia data such as images and videos. In terms of data privacy and storage optimization, the encrypted deduplication should be checked to save space overhead for cloud servers. Compared to exact deduplication, fuzzy deduplication is low-cost for encrypted multimedia data. Focusing on reducing the false deletion rate, an efficient and secure fuzzy deduplication system based on dual-feature without additional servers is proposed. We also propose a concept of pre-verification for label consistency to compensate for the loss that cannot be fixed through post-verification. Therefore, it is more practical. Finally, we conduct experiments on real-world datasets for performance evaluation. The experimental results show good performance in terms of both computational cost and deduplication efficiency.