Jiahang Sun, Zhiyong Wang, Runhan Yang, Chenjun Xiao, John C.s. Lui, Zhongxiang Dai. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Leader election serves a well-defined role in leader-based Byzantine Fault Tolerant (BFT) protocols. Existing reputation-based leader election frameworks for partially synchronous BFTs suffer from either protocol-specific proofs, narrow applicability, or unbounded recovery after network stabilization, leaving an open problem. This paper presents a novel protocol-independent abstraction formalizing generic correctness properties and effectiveness guarantees for leader election under partial synchrony, enabling protocol-independent analysis and design. Building on this, we design the Sliding Window Leader Election (SWLE) mechanism. SWLE dynamically adjusts leader nominations via consensus-behavior-based reputation scores, enforcing Byzantine-cost amplification. We demonstrate SWLE introduces minimal extra overhead to the base protocol and prove it satisfies all abstraction properties and provides superior effectiveness. We show, with a 16-server deployment across 4 different regions in northern China, SWLE achieves up to 4.2x higher throughput, 75% lower latency and 27% Byzantine leader frequency compared to the state-of-the-art solution under common Byzantine faults, while maintaining efficiency in fault-free scenarios.

As cryptocurrency prices continue to recover, crypto crimes such as money laundering are becoming increasingly rampant. Mixing services such as Tornado Cash have become the primary tools for obfuscating illegal financial transactions due to their inherent anonymity mechanisms. Tornado Cash is a non-custodial, smart contract-based mixing service (SC-CMS) that breaks the direct mapping between deposit and withdrawal accounts, hindering regulators from tracking illicit fund flows. Existing deanonymization methods for Tornado Cash suffer from several challenges, including vague theoretical concepts, evolving mixing mechanisms, and insufficient labeled samples. To address these concerns, this paper proposes the first formal concept of SC-CMS to facilitate and evaluate the deanonymization efforts systematically. We design a novel linkability attack, LASC, based on enhanced graph structure learning, to associate mixing accounts on Tornado Cash and mathematically prove its feasibility. Comprehensive experiments on real Ethereum transactions demonstrate that LASC outperforms state-of-the-art works in both performance and efficiency.

The contextual multi-armed bandit (MAB) is a widely used framework for problems requiring sequential decision-making under uncertainty, such as recommendation systems. In applications involving a large number of users, the performance of contextual MAB can be significantly improved by facilitating collaboration among multiple users. This has been achieved by the clustering of bandits (CB) methods, which adaptively group the users into different clusters and achieve collaboration by allowing the users in the same cluster to share data. However, classical CB algorithms typically rely on numerical reward feedback, which may not be practical in certain real-world applications. For instance, in recommendation systems, it is more realistic and reliable to solicit preference feedback between pairs of recommended items rather than absolute rewards. To address this limitation, we introduce the first "clustering of dueling bandit algorithms" to enable collaborative decision-making based on preference feedback. We propose two novel algorithms: (1) Clustering of Linear Dueling Bandits (COLDB) which models the user reward functions as linear functions of the context vectors, and (2) Clustering of Neural Dueling Bandits (CONDB) which uses a neural network to model complex, non-linear user reward functions. Both algorithms are supported by rigorous theoretical analyses, demonstrating that user collaboration leads to improved regret bounds. Extensive empirical evaluations on synthetic and real-world datasets further validate the effectiveness of our methods, establishing their potential in real-world applications involving multiple users with preference-based feedback.

With the help of artificial intelligence, the large amount of data generated by Internet of Things (IoT) has unleashed significant value. Federated learning is emerging as a novel paradigm which can be applied to solve the privacy issues caused by analyzing IoT data. However, traditional federated learning protocols are vulnerable to inference and poisoning attacks. Various solutions have been proposed to enhance data privacy and robustness. Nonetheless, most of these solutions are usually centralized and rely on unrealistic security assumptions. Furthermore, the recently proposed blockchain-based decentralized solutions generally incur high costs, which is unaffordable for resource-constrained IoT devices. In this article, we propose a practical secure federated learning system named PrSeFL. We utilize blockchain to decentralize the federated learning process so that the security assumptions are easier to achieve in practice. To preserve data privacy, we implement secure multiparty computation-based secure aggregation in blockchain environment. To guarantee practical robustness, we enforce norm constraints on the masked updates via zero-knowledge proof. Moreover, we propose a modified dynamic accumulator which is utilized to realize lightweight anonymous authentication of users. Simulation results show that, compared with state-of-the-art systems, PrSeFL has superior performance on authentication and model training. And the advantage of PrSeFL becomes more significant as the number of users grows.