Centralized trading platforms have long been the preferred choice for users, despite growing concerns regarding data privacy. Users have to place their trust in these platforms and provide sensitive personal information, like identities and financial accounts. However, these centralized platforms often lack transparency, making it challenging to ensure fairness, privacy, and security against both external and internal risks. In contrast, a decentralized fair trading paradigm, harnessing the potential of blockchain technology, is rapidly emerging. It empowers individuals to engage in the exchange of digital assets with others while guaranteeing fairness, efficiency, and privacy. In this paper, we conduct a comprehensive survey of decentralized fair trading. We commence by providing fundamental definitions of fair trading and tracing its evolution over time. We then delve into the essential framework of on-chain and off-chain trading and highlight key improvements that enhance the efficiency of decentralized fair trading within various application scenarios. Furthermore, we undertake a thorough analysis of privacy and security enhancements within the scope, summarizing defenses against known attacks. Finally, we outline the challenges and offer insights into the future prospects of decentralized fair trading, with the aim of inspiring the development of more innovative and promising designs in this evolving trend.

Recently, blockchain-based decentralized mobile crowdsensing systems have emerged to eliminate traditional centralized trust and to achieve transparent task assignments via smart contracts. It allows workers to select tasks freely, thereby maximizing their benefits. However, prior designs rarely considered the globally optimal task assignment that significantly impacts the efficiency and quality of task performance, like maximizing the task completion ratio and minimizing the total travel distance of workers. So in this paper, we propose DCrowd, a new blockchain-based mobile crowdsensing system, to realize the decentralized, transparent, and globally optimal task assignment. In brief, we first introduce the Proof of Task Assignment consensus mechanism. This allows miners to conduct globally optimal task assignments off-chain, leverages smart contracts to perform lightweight verification for task assignment results on-chain, and stores the globally optimal task assignment in a customized block. Then, we devise the Weight-Prioritized Task Selection strategy and Threshold-based Adaptive Minimum Cost Flow algorithm, to further optimize the system performance and guide miners in competing for minting rights. A thorough theoretical analysis is provided. Extensive experiments on real-world datasets indicate that DCrowd can reduce the broadcast and consensus latency by over 50% and improve the throughput by over 87% compared with existing systems.

Access control for data sharing among multiple autonomous organizations remains a challenging problem, as existing solutions typically rely on predefined static rules and cannot adapt well to such a scenario featured by hierarchical cross-domain data circulation with dynamic control contexts. While zero trust emerges as a promising tool to solve the problem, integrating it with a data-centric access control model that can dynamically generate context-aware policies and efficiently enforce trustworthy authorization and authentication is a non-trivial task. In this paper, we propose a self-adaptive and dynamic access control framework for cross-domain data sharing, which follows up zero-trust security principles and exploits blockchains coupled with rule learning to facilitate context-based access control. Specifically, our approach utilizes real-time contextual information for dynamic rule learning and compiles multiple rule clusters guided by Dempster-Shafer evidence theory to generate precise and adaptive control policies. Experimental results show that the proposed framework achieves desired security goals with a minimal performance overhead (approximately no more than 13 milliseconds), even for large-scale networks with high-concurrency tasks.

The rapid evolution of public knowledge is the trend of the present era; rendering previously collected data susceptible to obsolescence. The continuously generated new knowledge could further affect the performance of the model trained with previous data, such a phenomenon is called temporal misalignment. A vanilla mitigation approach is to periodically update the model in a centralized learning scheme. However, in a decentralized learning framework like Federated Learning (FL), such a patch requires clients to upload the data, which contradicts FL's intention to protect clients' privacy. Furthermore, considering the stationary defenses in FL, new knowledge could be misjudged and rejected as malicious attacks, which hinders the further update of the model. Yet dynamically adapting defenses requires meticulous fine-tuning and harms the scalability. Thus in this poster, we raise such practical concern and discuss it in the context of FL. We then build a prototype of a GPT2-based FL framework and conduct experiments to demonstrate our perspective. The performance in new knowledge drops by 33.47% compared with the previous data, which justify the FL with defenses strategy can misjudge the new knowledge.