Zhen Li

With the widespread adoption of cryptocurrencies, exemplified by Bitcoin, mixing services have been increasingly misused for illicit activities such as fraud, extortion, and money laundering. These services cut off the direct link between input and output addresses to conceal publicly available transaction traces on the blockchain, thereby evading regulatory investigations. In recent years, significant efforts have been made to deanonymize Bitcoin mixing services (BMS). While these efforts have yielded some achievements in service detection, they still face several challenges, such as vague theoretical definitions, coarse-grained identification capabilities, and insufficient labeling information. To address these challenges, this paper formalizes the notion of BMS with a syntax, security model, and goals, providing a universal definition for evaluating service anonymity and detection feasibility. We also propose a fine-grained detection framework for BMS, called BMS-FDET, which leverages heterogeneous Bitcoin transactional networks and multiple attention mechanisms to identify mixing transactions across different services. To improve the model's detection capabilities and compare performance, a sufficient ground-truth dataset with over 596,200 entries covering more than 15 mixing services is built from extensive data sources. Comprehensive experiments demonstrate the superiority of BMS-FDET over existing methods.

This paper presents Hydra, a dynamic BFT protocol that allows replicas to join and leave the system dynamically. It addresses the limitations of traditional static BFTs in managing membership changes and can be used to simplify the implementation of many features in modern blockchain applications. Hydra relies on weaker assumptions to achieve standard properties compared to the existing solution Dyno and introduces a configuration auto-transition protocol to ensure liveness. Through temporary configurations and explicitly defined replica responsibilities for request handling, Hydra pipelines membership requests alongside regular requests and realizes clarity, achieving a more efficient and smoother configuration transitions. It also employs a non-blocking configuration discovery mechanism, enabling new replicas to participate in consensus quickly. We formally prove Hydra's correctness under the dynamic BFT model. Experimental results demonstrate Hydra's ability to maintain throughput fluctuations within 5% during various replica join and leave scenarios, outperforming Dyno and existing BFT system supporting reconfiguration in both stability and efficiency. Hydra effectively manages scenarios that Dyno circumvents with stronger assumptions and quickly restores throughput to normal levels.

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.

This paper introduces the Deferred Byzantine Generals Problem, a variant of the Byzantine Generals Problem which focuses on ensuring replicas maintain consistency over timed-release secret operations (operations that can only be known after a specified time or event). The solution to the problem is called the Deferred Byzantine Fault Tolerant (DBFT) consensus. DBFT can operate exclusive or be interleave with BFTs to handle specific tasks at designated sequence numbers or views, thereby facilitating the implementation of certain system-desirable features or supporting novel applications. It does not rely on existing timed-release primitives, but instead ensures its timed-release property through voting interactions. We presents the system model of DBFT SMR under partial synchronization using Threshold Public Key Encryption (TPKE) as the cryptographic primitives, highlighting the core issues. Then we design and implement the DBFT protocol using PBFT notations, focusing on the unique parts to facilitate expansions to other paradigms. Through experimental results, we show the impact of different executing modes and parameter choices on performance and discuss potential optimizations.

This paper seeks to enhance the performance of large-scale leader-based Byzantine Fault Tolerant (BFT) systems by proposing a novel Group BFT scheme. The scheme utilizes a two-round message transmission process to distribute the load from a single leader across multiple replicas by dividing the entire consensus network into groups, each with an equal number of replicas. Each group has a leader to process the group's voting messages into a single aggregated voting message during the first round, which is then transmitted to the consensus leader in the second round (similar process for proposing). We establish a formal system framework for Group BFT protocols with a versatile set of base components and explicit definitions, addressing the challenges inherent in designing such a system. We further design and implement two highly efficient Group BFT protocols: one that supports inter-group member exchange and the other one that does not. We theoretically prove the safety, liveness, and responsiveness of the Group BFT protocols. We conduct a formal analysis of Group BFTs' tolerance and complexity. Experimental results show that the two Group BFT protocols significantly alleviate the processing bottlenecks of the leader and highly improve throughput in large-scale systems.