Group data sharing in cloud environments has become a hot topic in recent decades. With the popularity of cloud computing, how to achieve secure and efficient data sharing in cloud environments is an urgent problem to be solved. In addition, how to achieve both anonymity and traceability is also a challenge in the cloud for data sharing. This paper focuses on enabling data sharing and storage for the same group in the cloud with high security and efficiency in an anonymous manner. By leveraging the key agreement and the group signature, a novel traceable group data sharing scheme is proposed to support anonymous multiple users in public clouds. On the one hand, group members can communicate anonymously with respect to the group signature, and the real identities of members can be traced if necessary. On the other hand, a common conference key is derived based on the key agreement to enable group members to share and store their data securely. Note that a symmetric balanced incomplete block design (SBIBD) is utilized for key generation, which substantially reduces the burden on members to derive a common conference key. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for group data sharing in cloud computing.

The multimodal optimization problem (MMOP) requires the algorithm to find multiple global optima of the problem simultaneously. In order to solve MMOP efficiently, a novel differential evolution (DE) algorithm based on the local binary pattern (LBP) is proposed in this paper. The LBP makes use of the neighbors' information for extracting relevant pattern information, so as to identify the multiple regions of interests, which is similar to finding multiple peaks in MMOP. Inspired by the principle of LBP, this paper proposes an LBP-based adaptive DE (LBPADE) algorithm. It enables the LBP operator to form multiple niches, and further to locate multiple peak regions in MMOP. Moreover, based on the LBP niching information, we develop a niching and global interaction (NGI) mutation strategy and an adaptive parameter strategy (APS) to fully search the niching areas and maintain multiple peak regions. The proposed NGI mutation strategy incorporates information from both the niching and the global areas for effective exploration, while APS adjusts the parameters of each individual based on its own LBP information and guides the individual to the promising direction. The proposed LBPADE\pagebreak algorithm is evaluated on the extensive MMOPs test functions. The experimental results show that LBPADE outperforms or at least remains competitive with some state-of-the-art algorithms.

Industrial Internet of Things (IIoT) is significant of building powerful industrial systems and applications. Deep learning has provided a promising opportunity to extract useful knowledge by utilizing vast amounts of data in IIoT. However, lacking of massive public datasets will lead to low performance and overfitting of the learned model. Therefore, the federated deep learning over distributed datasets has been proposed. Whereas, it inevitably introduces some new security challenges, i.e., disclosing participant's data privacy. However, existing methods cannot guarantee each participant's data privacy in a learning group. In this article, we propose two privacy-preserving asynchronous deep learning schemes [privacy-preserving and asynchronous deep learning via re-encryption (DeepPAR) and dynamic privacy-preserving and asynchronous deep learning (DeepDPA)]. Compared to the state-of-the-art work, DeepPAR protects each participant's input privacy while preserving dynamic update secrecy inherently. Meanwhile, DeepDPA enables to guarantee backward secrecy of group participants in a lightweight manner. Security analysis and performance evaluations on real dataset show that our proposed schemes are secure, efficient, and effective.

Federated learning is a collaborative machine learning framework where a global model is trained by different organizations under the privacy restrictions. Promising as it is, privacy and robustness issues emerge when an adversary attempts to infer the private information from the exchanged parameters or compromise the global model. Various protocols have been proposed to counter the security risks, however, it becomes challenging when one wants to make federated learning protocols robust against Byzantine adversaries while preserving the privacy of the individual participant. In this article, we propose a differentially private Byzantine-robust federated learning scheme (DPBFL) with high computation and communication efficiency. The proposed scheme is effective in preventing adversarial attacks launched by the Byzantine participants and achieves differential privacy through a novel aggregation protocol in the shuffle model. The theoretical analysis indicates that the proposed scheme converges to the approximate optimal solution with the learning error dependent on the differential privacy budget and the number of Byzantine participants. Experimental results on MNIST, FashionMNIST and CIFAR10 demonstrate that the proposed scheme is effective and efficient.

Data deduplication and public auditing are significant for providing secure and efficient network storage services. However, the existing data deduplication schemes supporting auditing not only cannot effectively alleviate the threats of the single point of failure and duplicate-faking attack, but also have to bear the massive waste of computation and storage resources caused by metadata redundancy and repetitive audit tasks. In this paper, we propose a blockchain-based secure deduplication and shared auditing scheme in decentralized storage. Specifically, our scheme utilizes a novel deduplication protocol based on the double-server storage model to achieve efficient space-saving while protecting data users from losing data under a single point of failure and duplicate-faking attack. Besides, it sharply reduces the computation and storage costs of metadata by introducing a lightweight authenticator generation algorithm and update protocol. On this basis, our scheme further adopts a blockchain-based two-way shared auditing mechanism to achieve decentralized public auditing without the third-party auditor, in which the audit authenticators and results of outsourced data are shared among its users to avoid repetitive audit tasks. Security and performance analysis indicates the practicability of our scheme.