Sharding is a promising solution to enhance blockchain scalability. While deploying more shards of smaller sizes for a given network scale can significantly boost performance, it also heightens the risk of shard failures. In many existing sharding systems, the failure of a single shard can compromise the entire system. Therefore, to ensure safety, current systems often require each shard to contain hundreds of consensus nodes to prevent crashes, adversely affecting scalability. In this paper, we propose Levee, a blockchain sharding system capable of tolerating shard failures. When a shard malfunctions, Levee can swiftly detect, isolate, and autonomously recover the faulty shard, allowing other shards to operate without interruption. This fault-tolerance feature enables Levee to reduce shard sizes by 73.7% and increase the number of shards by 3.25 times, all without sacrificing security. When tested in a scenario with 7000 nodes, Levee demonstrated a 14.34 times increase in throughput and a 65% decrease in transaction latency compared to traditional non-fault-tolerant sharding systems.

Cross-chain swaps are a crucial application that facilitates the transfer of digital assets across different blockchains, thereby enhancing the flexibility and availability of asset circulation. Ensuring atomicity is a fundamental objective for cross-chain swaps. The Hashed TimeLock Contract (HTLC) protocol is one of the primary solutions for cross-chain swaps. It ensures atomicity by statically partitioning execution actions for swap submission and asset refund along the time dimension. Such designs rely on a predictable upper bound on transaction confirmation time. However, this assumption does not hold in practical blockchain environments, leading to atomicity violations. To address this limitation, we propose Chuchu, a hashlock group protocol for cross-chain swaps. Chuchu abandons static partitioning along the time dimension and instead distinguishes swap submission and asset refund through explicitly defined asset locking states and their dynamic transitions. To cope with divergent execution progress across blockchains, Chuchu bounds execution progress divergence and introduces an exit-proof mechanism, ensuring that locked assets always have well-defined and consistent unlocking paths under all execution scenarios. The effectiveness and feasibility of Chuchu are demonstrated through theoretical analysis and formal verification using TLA+. Meanwhile, experiment results show that Chuchu reduces execution time by over 98% compared to the HTLC protocol in the case of swap rollbacks.

In recent years, graph neural networks have demonstrated strong capabilities in processing graph-structured data and have made significant progress in the field of smart contract vulnerability detection. This paper introduces HF-Sec, a novel framework for smart contract vulnerability detection. The framework first automatically generates heterogeneous contract graphs from the source code of Ethereum smart contracts to represent the control flow and function call relationships of the code. Then, by using a multi-source attention mechanism, the framework is able to synthesize features from different sources to capture key information from multiple perspectives. In addition, HF-Sec utilizes Fast Graph Transformer Networks and Kolmogorov-Arnold Networks to automatically extract mission-critical meta-paths and enhance the interpretability of the model. We performed experimental validation on a mixed dataset containing 423 contracts with vulnerabilities and 2742 contracts without vulnerabilities. The experimental results show that HF-Sec can significantly improve the accuracy of smart contract vulnerability detection, which is better than the methods based on machine learning or traditional analysis techniques. Through a series of ablation experiments, we further verified the importance of various key components in HF-Sec to improve the detection accuracy.

Rollup stands out as one of the most effective techniques for blockchain Layer-2 scaling. By processing transactions off-chain, it significantly enhances the throughput. However, the most rollup implementations currently rely on centralized sequencers, exposing the system and users to censorship attacks and risking network paralysis. In contrast, fully decentralized sequencers encounter latency issues and reduced throughput during the consensus phase. We propose a multislot weighted leader election algorithm based on shared sequencers, apply the proposer–builder separation (PBS) model, and use the fuzzy cognitive map (FCM) to analyze and optimize the important influence parameters. With its low trust dependence and high functionality, the probability of selecting malicious nodes is reduced. The sequencing and consensus are separated, so that the transaction can quickly reach soft confirmation. We implement this algorithm in a shared sequencer prototype. The experimental results show that the proposed algorithm parameter settings are in line with the expectations, and the probability of electing malicious nodes is significantly reduced. The transactions per second (TPS) of the network can cope with the throughput requirements of the Layer-2.

The account allocation mechanism is a crucial component affecting the performance of sharding blockchain systems. A well-designed account allocation mechanism must reduce the number of cross-shard transactions while balancing the workload across shards. State-of-the-art mechanisms, which are semi-static, typically adjust account partitions based on historical transactions at regular intervals. However, in real-world applications, unpredictable new scenarios in historical transactions or sudden workload changes can impact shard performance. These existing mechanisms can neither foresee such scenarios to avoid cross-shard transactions nor dynamically adjust account partitions to improve workload issues, leading to suboptimal performance until the next re-allocation. To this end, we propose Orbit, a dynamic account allocation mechanism based on the pending transactions in the pool. Orbit can promptly detect new situations and changes in pending transactions and provide updated allocation strategies. Moreover, through its off-chain scheduling mechanism, Orbit can deploy these strategies before transaction packaging to enhance shard performance. Experimental results show that compared to the state-of-the-art allocation mechanisms, Orbit improves throughput by 2.02 times, reduces cross-shard transactions to 11.9%, and achieves a more balanced shard workload. Additionally, Orbit excels in various other aspects, including latency, transaction queue size, and bandwidth overhead, outperforming the state-of-the-art mechanisms.

Sharding is one of the most promising technologies for significantly increasing blockchain transaction throughput. However, as the number of shards increases, the ratio of cross-shard transactions in existing blockchain sharding protocols gradually approaches 100%. Since cross-shard transactions consume many times more resources than intra-shard transactions, the processing overhead of cross-shard transactions already accounts for the majority of the total overhead of the sharding system. There is a very large gap between the transaction throughput of the sharding system and its theoretical upper limit. In this article, we propose Estuary, a novel low cross-shard blockchain sharding protocol. Taking the state model as an entry point, Estuary designs a multi-level state model and state splitting and aggregation mechanism. It decouples the identity and quantity of state units, enabling transactions between users to be completed within one shard. Only when the state quantity for all shards of a user is insufficient a small number of cross-shard transactions are required. On this basis, we propose a community overlap propagation algorithm for sharding. It defines the users’ belonging coefficients of each shard and optimizes the state distribution so that the state distribution can better match the transaction characteristics between users. Finally, we develop an analysis framework for the sharding protocol and experiment with real Bitcoin transactions. The evaluation results show that compared to the state-of-the-art sharding protocol, Estuary reduces the ratio of cross-shard transactions by 88.54% and achieves more than 1.85 times the throughput improvement (92.98% of the theoretical upper limit).

Sharding is a prevalent approach for addressing performance issues in blockchain. To reduce governance complexities and ensure system security, a common practice involves a relay chain to coordinate cross-shard transactions. However, with a growing number of shards and cross-shard transactions, the single relay chain usually first suffers from performance bottleneck and shows poor scalability, thus making the relay chain's scalability vital for sharding systems. To solve this, we proposeMitosis, the first multi-relay architecture to improve the relay chain's scalability by sharding the relay chain itself. Our proposed relay sharding algorithm dynamically adjusts the number of relays or optimizes the topology between relays and shards to adaptively scale up relay chain's performance. Furthermore, to guarantee the security of the multi-relay architecture, a new validator reconfiguration scheme is designed, accompanied by a comprehensive security analysis ofMitosis. Through simulation experiments on two mainstream relay chain paradigms, we demonstrate thatMitosiscan achieve high scalability and outperform state-of-the-art baselines in terms of workload of relays, relay chain throughput, and transaction latency.

Consensus algorithm is the core component of consortium blockchains. Equality, Unpredictability and Scalability are three important demands for the consensus algorithms of consortium blockchain. Existing deterministic consensus algorithms (e.g. PBFT) can ensure Equality, but cannot meanwhile meet Unpredictability and Scalability; probabilistic consensus algorithms (e.g. PoW) can achieve Scalability and guarantee a decent Unpredictability, but cannot meet the Equality requirement. In this paper, we propose a new consensus algorithm, namely Themis, which takes the three properties into account. Themis independently adjusts the block-producing difficulty of each node through a self-adaptive node election mechanism, effectively reducing the correlation between the block-producing frequency and the invested computing power of each node. Besides, a GEOST main chain consensus rule is proposed to handle forks and further improve the performance of the algorithm. If a fork occurs, consensus nodes will choose the sub-chain with the highest Equality to join the main chain. Evaluations show that Themis achieves outstanding performance in Equality and Unpredictability while ensuring Scalability, compared with the existing algorithms.