Sequential recommender systems aim to recommend the next items in which target users are most interested based on their historical interaction sequences. In practice, historical sequences typically contain some inherent noise (e.g., accidental interactions), which is harmful to learn accurate sequence representations and thus misleads the next-item recommendation. However, the absence of supervised signals (i.e., labels indicating noisy items) makes the problem of sequence denoising rather challenging. To this end, we propose a novel sequence denoising paradigm for sequential recommendation by learning hierarchical item inconsistency signals. More specifically, we design a hierarchical sequence denoising (HSD) model, which first learns two levels of inconsistency signals in input sequences, and then generates noiseless subsequences (i.e., dropping inherent noisy items) for subsequent sequential recommenders. It is noteworthy that HSD is flexible to accommodate supervised item signals, if any, and can be seamlessly integrated with most existing sequential recommendation models to boost their performance. Extensive experiments on five public benchmark datasets demonstrate the superiority of HSD over state-of-the-art denoising methods and its applicability over a wide variety of mainstream sequential recommendation models. The implementation code is available at https://github.com/zc-97/HSD

With the rapid deployment of Internet of Things (IoT) devices in various industries and fields, the massive amount of data produced by these devices can yield greater value through sharing. A critical challenge in the data-sharing process is ensuring that the data are high quality. However, the quality of data provided by a large number of IoT devices is impacted by the variability of factors contributing to the data quality (DQ). Effective and safe sharing of perception data by the limited resources of IoT devices is a problem worth investigating. In this article, we propose a smart contract-based and DQ-driven incentive mechanism. First, a smart contract is proposed to realize security in the data-sharing process, while the proposed DQ evaluation mechanism ensures the quality of the shared data. Second, a two-layer Stackelberg game of nested coalitional (TLSNC) scheme is designed to obtain the maximum overall social welfare according to the trust score obtained during DQ evaluation while satisfying the limitation of loose and insufficient computing resources. Moreover, we designed a smart contract for automatic execution of the data-sharing transaction and used a trusted execution environment (TEE) to complete the security calculation of shared data. Finally, the numerical results reveal the effectiveness of the DQ evaluation mechanism and the security of our TEE-based model. Based on the proposed scheme, sustainable incentives for user participation and high-quality data sharing can be achieved. In addition, our system can significantly improve the overall social welfare compared to traditional solutions.

To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm’s complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency.

The emerging metaverses can be regarded as the current wave of the digital civilization revolution due to it build a new connection between the physical and virtual space, and such connections make it possible for virtual space to interact with real word. However, some concerns, such as privacy data leakage incedents, still hinder the widespread deployment of the metaverse. The blockchained federated learning (FL) architecture can train a federated global model towards multiple stakeholders without the need for centralized management servers and the risk of privacy data leaking as a new paradigm to address the metaverse dilemma. Although many studies have been proposed for the great development potential of blockchain and federated learning for metaverse, few studies, if not none, have explored security threat against the blockchained FL architecture. This work report a prominent phenomenon that the combination of FL and blockchain may pose a more serious security threat, and first to propose Collusion-based Poisoning Attacks (CPA) in the blockchained FL architecture. To be specific, the malicious participants manipulate the model training process so that poisoned global models are aggregated with the most significant probability during the FL training phase. Then submits to the blockchain system and enables the poisoned models to be affected for all participants due to the strong consistency of blockchain. Experimental analysis demonstrates that the proposed CPA can achieve superior performance on multiple aspects and has better performances than the state-of-the-art attack method on stealth and robustness. This research can shed light on the hidden vulnerabilities of the blockchained FL architecture and lay the foundation for build a more open and secure metaverse.