Distributed Ledger Technology (DLT) has gained significant attention due to its potential for enabling secure and decentralised systems. However, this premise can only be achieved if information asymmetry is eliminated and there is equal and simultaneous access to consistent and transparent data by all participants in the network. In this article, we explore various DLT information symmetry challenges, including theoretical fundamentals and practical hurdles in relation to the technology stack. We explore the potential of using DLT to facilitate mutual knowledge sharing in E-Commerce scenarios via Proofs of Information Symmetry (PoIS). We found that PoIS have the ability to significantly improve trust between transacting parties, specifically in the context of producer-consumer relationships and associated investor-asset management and contract obligations. In addition, we discovered that the effectiveness of PoIS strongly depends on the presence of a reliable notification system. Our study found that the utilisation of Zero-Knowledge (ZK) proofs can improve confidence among transaction partners, particularly in situations where safeguarding trade secrets or maintaining the privacy of agents is necessary. Our results are demonstrated by deriving from testing PoIS in a commercial context that includes a vehicle under a short-term lease management setup. This evaluation demonstrates the benefit of a contextual ZK proof that maintains the individual privacy of trading partners.

This paper designed and implemented a blockchain-based multisignature approach to digitally transform supply chain governance in multi-tier food supply chains, particularly in a geographically dispersed beef supply chain. An exploratory case study was utilised to demonstrate the design, implementation, and evaluation of a blockchain-based multisignature approach that was deployed on the Smart Trade Networks (STN) Proof of Authority (PoA) blockchain system for data collection and validation in a beef supply chain context. The multisignature approach was implemented with a use case to track a shipment of 92 cattle and meat products through key events from farm to food service. The use-case deployment records approximately 6000 data points registered on the STN PoA blockchain system. The real-case deployment illustrates the capability of the blockchain-based multisignature approach to digitally improve beef supply chain governance by enabling whole-of-chain transparency and trustworthy information sharing and supports supply chain professionals to have a better understanding of how to unlock blockchain potential for supply chain transformation.

Smart speakers are becoming increasingly popular and permeate many aspects of human life. To improve the security of smart speakers, voice commands transmitted over a network are encrypted; however, user privacy issues related to smart speakers continue to emerge. In fact, attackers are still able to infer the content of a user’s specific voice commands from encrypted traffic through machine learning methods to obtain private information for advertising or to carry out malicious attacks. This traffic analysis attack is referred to as a voice command fingerprinting attack. In recent years, research on improving the accuracy of voice command fingerprinting attacks has become a hot topic and remains a challenging task. To improve the accuracy of voice command fingerprinting attacks, we design a new method in this paper. We use an adaptive and dilated residual network to process spatial features. In addition, we find that using temporal features helps improve fingerprinting attack accuracy, and therefore design an attention-based bidirectional gated recurrent unit. Then, we effectively combine the two models. Our method achieves an accuracy greater than 93.36% in a closed-world scenario, which exceeds those of other state-of-the-art methods (2020 WiSec Wang et al.). In a more realistic open-world setting, our model is still effective, obtaining a true-positive rate of 99.50% and a false-positive rate of 0.1% compared to Sirinam et al.’s rates of 90.66% and 0.1%, respectively. We also demonstrate that our model has good generalizability, as our model can also be applied to website fingerprinting and outperforms 2018 CCS Sirinam et al.