In IoT-enabled electricity markets, trading is often conducted in a forward-style manner, where parties agree on prices and quantities in advance and settle upon future delivery. However, deploying such forward-style exchanges across chains is incompatible with execution-restricted blockchains and typically incurs significant cross-chain gas overhead. Moreover, existing low-overhead cross-chain mechanisms, particularly HTLC-based schemes, inherently rely on pairwise locking and thus cannot efficiently support one-to-many settlement. We propose an inter-chain forward-style exchange framework based on a dual-track validation architecture. The framework integrates a double-spend fraud-proof mechanism to ensure correctness and a proof-of-acceptance (PoAc) mechanism to reduce cross-chain overhead for successful settlements. We further introduce a novel k-directional hash lock enabling one-to-many settlement, allowing a single buy order to be split across multiple sellers. Our analysis shows that the framework resists griefing attacks, prevents adversarial losses, and preserves cross-chain atomicity. Experiments demonstrate support for execution-restricted blockchains such as Bitcoin, scalability to 862,000 sellers per order, and a 78.25% reduction in settlement cost compared to light-client approaches.

Transaction propagation delay limits the block interval and is one of the main bottlenecks in improving Bitcoin throughput. However, transaction relay in Bitcoin is entirely voluntary, which results in low bandwidth and high transaction propagation delay. Improving relay motivation by introducing incentives can effectively reduce delay, but it still faces challenges such as Sybil attacks during reward allocation, leakage of network layer privacy, and high on-chain/off-chain overhead. Therefore, this paper proposes Txtail, a practical transaction relay incentive scheme for Bitcoin, based on continuously attaching relay evidence representing the relays’ identity and contribution during transaction propagation. We employ a free pricing mechanism based on the game between relays to allocate rewards fairly. We design an order-insensitive relay evidence structure based on aggregate signatures and public key mapping, which reduces off-chain data overhead while alleviating the leakage of relay paths by obfuscating the relay order. We construct a verifiable lottery mechanism based on Merkle tree commitments to reduce the data that needs to be uploaded to the chain. Both theoretical and experimental results show that Txtail reduces the per-hop off-chain overhead and the overall on-chain overhead by 96.6% and 79.8%, respectively, compared with state-of-the-art baselines, while remaining practical for deployment.

Tor's original design does not have an incentive mechanism but relies on volunteers to maintain their relay nodes for free, eventually leading to the current situation of centralization and lack of relay nodes. Current incentive schemes designed for Tor generally rely on centralized roles, thus presenting a risk of destroying Tor's anonymity. This paper proposes R2E, a decentralized scheme that treats Tor relay services as cryptocurrency mining and rewards the relays with generated tokens while addressing the challenge of how to design decentralized protocols that quantify workload while ensuring fairness and anonymity. We construct the Proof-of-Relay protocol in R2E that enforces random circuit selection, limits the number of nonce attempts, and exploits one-time keys and zero-knowledge proofs to protect participants' identities. We implemented a prototype of R2E based on Ethereum and conducted the trial operation and several confirmation experiments involving$2^{20}$clients,$2^{10}$to$2^{16}$nodes, and 256 circuits for each client to demonstrate its applicability. Analysis and experimental results show that R2E can effectively ensure the anonymity of participants' identities and fairness of incentive allocation while showing good performance in overhead and scalability, making it easy to be quickly applied in practical deployments.

Consensus protocols play a crucial role in determining the security and performance of blockchain systems, with committee-based consensus protocols being particularly important, especially in sharding consensus protocols. Anonymous election of committee nodes can mitigate DDoS attacks and bribery attempts. This approach can also be applied to sharding systems to mitigate the risk associated with a single vulnerable shard. However, current node secret selection schemes still present remaining issues. Single secret leader election schemes struggle to elect multiple leaders with equal anonymity, and existing secret committee election schemes lack adequate measures for tracking malicious nodes. To address these issues, we propose accountable secret committee election schemes that not only regulate the number of nodes but also maintain anonymity during the phases of leader proposal and verifier voting. Furthermore, our schemes enable the tracing of malicious nodes in a threshold way. In addition, we introduce two efficient threshold traceable membership proof schemes for both ad hoc and interactive scenarios. Unlike traceable ring signatures, our scheme can trace malicious nodes even after a single malicious behavior. Subsequently, we apply the accountable secret committee election scheme to sharding blockchains and devise a fully accountable anonymous consensus protocol. The experiment demonstrates that this protocol can elevate the difficulty of corrupting a single shard to the level of compromising the entire system, thereby significantly enhancing the security of the sharding system.

Secret leader election in consensus could protect leaders from Denial of Service (DoS) or bribery attacks, enhancing the blockchain system security. Single Secret Leader Election (SSLE), proposed by Boneh et al., supports electing a single random leader from a group of nodes while the leader’s identity remains secret until he reveals himself. Subsequent research endeavors have introduced distinct approaches to realize SSLE, yet most of these solutions consume relatively high communication complexity. In this paper, we propose an extended SSLE scheme, Secret Multiple Leaders Election (SMLE), based on linkable membership proof. A general SMLE scheme supports the one-time election of multiple consecutive secret leaders while reducing the average communication cost of a single leader election to constant complexity. In particular, SMLE is proven to satisfy a newly proposed consistent unpredictability property for each leader. Specifically, two concrete SMLE constructions are constructed. The first construction is designed for non-interactive scenarios where pre-configured system nodes are not required. The second one is designed for interactive scenarios where nodes operate within a committee. Furthermore, we extend SMLE to Secret Committee Election (SCE) and realize the anonymous node allocation in sharding blockchains utilizing SCE, thereby significantly enhancing the security of the sharding system. Finally, the experimental results indicate that our constructions exhibit minimal communication and computational overhead. When integrated into sharding systems, our protocol could increase an adversary’s attack difficulty, with the enhancement proportion approximately equal to the shard number.

Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Therefore, it is important to predict potential drug interactions since it can provide combination strategies of drugs for systematic and effective treatment. Existing deep learning-based methods often rely on DDI functional networks, or use them as an important part of the model information source. However, it is difficult to discover the interactions of a new drug. To address the above limitations, we propose a geometric molecular graph representation learning model (Mol-DDI) for DDI prediction based on the basic assumption that structure determines function. Mol-DDI only considers the covalent and non-covalent bond information of molecules, then it uses the pre-training idea of large-scale models to learn drug molecular representations and predict drug interactions during the fine-tuning process. Experimental results show that the Mol-DDI model outperforms others on the three datasets and performs better in predicting new drug interaction experiments.