Distributed Key Generation (DKG) enables threshold cryptographic key establishment without trusted third parties. Adapting DKG to asynchronous networks (ADKG) presents challenges. This paper addresses two critical limitations: (i) fixed thresholds, where each set of t+1 participants is identical, lacking the flexible configurations needed for asynchronous environments; and (ii) high complexity, stemming from parallel asynchronous binary agreement (ABA) instances during consensus.We propose FlexiADKG, an ADKG protocol achieving configurable threshold structures via vector space secret sharing, enabling heterogeneous authorization policies based on node attributes. We further present FlexiADKG+, an enhanced construction supporting composite access structures by unifying multiple authorization policies into a single MSP instance. Both protocols replace n parallel ABA instances with signature-free multivalued validated Byzantine agreement (MVBA), reducing round complexity from O(logn) to O(1).Implementation on geographically distributed AWS infrastructure demonstrates performance advantages: FlexiADKG achieves 61% runtime and 53% bandwidth consumption on average compared to state-of-the-art ADKG (SP’22), while FlexiADKG+ maintains practical efficiency with additional expressiveness for composite access structures. We provide formal security proofs validating all claimed properties for both constructions.

The rapid development of the automotive industry and the Internet of Vehicles (IoV) has led to an exponential growth of distributed vehicular data, driving the need for secure and efficient collaborative machine learning solutions. However, existing distributed collaborative machine learning (DCML) approaches, such as federated learning and split learning, face significant challenges in IoV scenarios, including limited training efficiency, centralized aggregation vulnerabilities, and constrained privacy and model protection. To address these issues, we propose a blockchain-based split federated learning (BSFL) scheme for IoV applications. BSFL non-trivially combines federated learning and split learning to enable vehicles with low computational power to participate in parallel training, improving both model accuracy and training efficiency. By utilizing blockchain as a decentralized infrastructure, BSFL eliminates the risks of single points of failure and ensures model consistency through Byzantine fault-tolerant consensus. Furthermore, we design a noise addition mechanism based on differential privacy to safeguard client data privacy and model security. Formal security analysis and extensive experiments demonstrate that BSFL achieves enhanced privacy, security, and training performance. Comparing to related DCML schemes, BSFL reduces computational overhead by up to 88.84% and client training time by up to 29.49% while maintaining comparable accuracy. When training on ResNet-50 based on CIFAR10, BSFL achieved an accuracy of 93.15%. And the verification process for each model’s training results on the blockchain requires 1.49 ms.

Dynamic proactive secret sharing (DPSS) is essential for distributed systems, enabling long-term key escrow, BFT protocol reconfiguration, and confidential state machine replication. Yet existing asynchronous schemes, while crucial for realistic settings, suffer from high communication overhead and poor practicality, limiting real-world deployment. We propose PlainDrop, a concise and efficient DPSS protocol designed specifically for asynchronous networks. PlainDrop achieves optimized communication complexity of $O(n<^>{2})$ via commitment-share decoupling combined with homomorphic threshold encryption techniques. PlainDrop also eliminates the need for expensive distributed key generation (DKG) and complex bivariate polynomial structures by introducing a lightweight silent setup framework and employing direct share processing based on univariate polynomials. We formally prove that PlainDrop provides secrecy, integrity, and termination in asynchronous networks against a mobile adversary corrupting up to one third of the parties. We implement PlainDrop and evaluate it on Amazon EC2 with up to 100 nodes. Our experimental results demonstrate average reductions of 37% and 67% in completion time, and 61% and 89% in communication volume, compared to DyCAPS and LongLive, respectively.

As blockchain technology advances, an increasing number of applications require interactions between smart contracts across multiple blockchains. However, existing cross-chain solutions exhibit limited scalability due to heterogeneous blockchain environments and diverse application requirements. A fundamental challenge lies in the absence of a unified resource definition for cross-chain processes, impeding moderate resource allocation and effective conflict resolution. Specifically, when extended to general cross-chain transactions involving invocations among multiple contracts, these methods lack the capability to correctly handle state transitions for all related contracts. This paper proposes AtomXross, a novel cross-chain scheme that supports arbitrary combinations of smart contracts during the cross-chain process. We build a scalable cross-chain architecture based on a relay chain and a cluster of cross-chain nodes to provide better scalability. We propose a unified definition for cross-chain resources within the system and implement an adaptive resource management mechanism on the relay chain, enabling it to record the invocation relationships of contract functions. When a cross-chain transaction involves multiple contract calls, AtomXross can index the calls and generate the corresponding call tree. To address the challenges posed by potential mutual invocations between smart contracts, we design an atomic transaction protocol based on buckle-lock, an ordered two-tier pessimistic locking mechanism. AtomXross ensures that cross-chain transactions do not conflict with each other while remaining compatible with non-cross-chain calls that may occur at any time. Furthermore, we propose a universal programming template for on-chain smart contracts, which enables ordinary smart contracts to acquire cross-chain capabilities. We implement AtomXross based on Hyperledger Fabric and FiscoBCOS. In comparison to WeCross, AtomXross lowers the gas cost on system initialization and incurs only a 14% increase in transaction latency while supporting inter-contract calls.

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.

With the development of air transportation, Space-Air-Ground Integrated Network (SAGIN) are playing an increasingly important role in optimizing air traffic management and enhancing flight safety for billions of passengers and trillions dollars of aviation industry. As the key technology of SAGIN, the Automatic Dependent Surveillance-Broadcast (ADS-B) system is widely used due to its simple operation, low construction cost, and high information accuracy. However, the security problems in ADS-B system, including lack of identity authentication between all communication links, crucial information transmitted in plaintext, and susceptibility to the single point of failure, have been serious obstacle to its wide application. Existing solutions fail to account for the unique characteristics of ADS-B and SAGIN, leading to inadequate security and poor performance in these specialized contexts. Aiming to solve the above issues and provide security and scalability for ADS-B system, we conduct the following research. Firstly, an enhanced identity-based broadcast signcryption (e-IBBSC) scheme is designed to keep crucial information confidential and all messages authenticated simultaneously. Secondly, we propose an efficient batch message authentication method combined with the Merkle tree and proposed e-IBBSC, significantly improving the ADS-B message utilization ratio from 1.35% to 74.10%. Thirdly, we utilize the sharding blockchain and Byzantine fault tolerance protocol to design the first sharding-based distributed management system for SAGIN that realizes fault tolerance and scalability. Finally, after a detailed security analysis and comprehensive performance evaluation, we demonstrate that our solution can achieve all proposed system goals including security, scalability, and high performance of 1s flight transaction processing latency and 62KTPS throughput.

Blockchain is facing scalability challenges. Rollups, as an off-chain scaling scheme, relieve the pressure on the main chain by transferring transactions to the layer 2 (L2) network. However, centralized sequencers remain vulnerable to single points of failure and weak censorship resistance. In this paper, we propose a decentralized rollup sequencer based on an optimized Tendermint consensus mechanism. This scheme effectively reduces the predictability of candidate node weights by integrating an enhanced Tendermint Weighted Round-Robin (WRR) algorithm with staking bucket splitting and randomized mapping strategies, while simultaneously incorporating a parallel transaction packing scheme to optimize the voting process. Furthermore, the proposed suspicious labeling mechanism combines on-chain challenge and fast rollback to improve transaction legitimacy verification. Experimental results show that the optimized system maintained 824 transactions per second (TPS) in a 30-node virtual machine environment, validating the scheme’s effectiveness in fairness and Sybil attack resistance. Additionally, the economic model and reward/punishment mechanisms enhanced decentralization, offering a new avenue for the extension of rollup technology.

Long-range attacks constitute a fundamental weakness of Proof-of-Stake (PoS) blockchains: once signing keys are leaked or obtained, fabricating a convincing alternative chain history becomes effectively costless. To mitigate this issue, we introduce a sampleable verifiable delay function (sVDF) that embeds compact, verifiable time samples into block headers and drives a time-aware fork-choice rule that lexicographically prioritizes cumulative sVDF iterations. We formalize the threat model and protocol interfaces, prove that our sVDF satisfies correctness, uniqueness, and sequentiality under standard cryptographic assumptions, and show that the augmented protocol increases the amount of sequential work required for a successful long-range forgery. Our experimental analysis indicates that moderate hardware heterogeneity and periodically refreshed anchors suffice to keep the catch-up probability low within realistic regimes. Our design is backward compatible, incurs modest bandwidth and verification overhead, and can serve as an additive defense for existing PoS blockchains.

A structured vector range argument proves that a committed vector v lies in a well-structured range of the form [0,2^d-1] . This structure makes the protocol extremely efficient, although it cannot handle more sophisticated range assertions, such as those arising from non-membership attestations. To address this gap, we study a more general setting not captured by prior constructions. In this setting, for each i, the admissible integer set for v_i is a union of k intervals _i def=⋃ _j=0^k-1[ l_i,j,r_i,j] . In this work, we present novel techniques to prove that v∈ℤ^n_p lies within _0 ×_1 ×⋯×_n-1 . We first introduce , a generic compiler that lifts a structured vector range argument to support such unstructured range assertions. Then we present , a realization of over the -based vector commitment scheme. achieves succinct communication and verifier time; its prover complexity is O( n log Nloglog N·log (nlog Nloglog N) ) , where N upper bounds the maximum interval size across all _i . Notably, is interval-agnostic, meaning its prover complexity is independent of the number of intervals k; therefore, its prover cost matches the single-interval case even when each _i is composed of hundreds of thousands of intervals. We also obtain two new structured vector range arguments and a batching-friendly variant of the ^+ lookup argument (PKC’24), which are also of independent interest. Experiments show that outperforms well-known curve-based vector range arguments on standard metrics while supporting strictly more expressive range assertions.

Byzantine Fault Tolerance (BFT) protocols are a critical research area in distributed systems and blockchain consensus due to their capacity to deliver high throughput and low latency. Traditional BFT protocols typically rely on a single leader to propose transactions and aggregate votes, which often creates a bottleneck due to the leader’s limited communication and computational capacities. The introduction of multi-leader BFT has the potential to mitigate this issue by increasing system parallelism. However, existing approaches fail to address the challenge of electing multiple leaders and lack a comprehensive analysis of the relationship between the number of leaders, security constraints, and system throughput. In this paper, we study the performance and security of multi-leader BFT protocols. Initially, we introduce a secret multi-leader election method resistant to corruption attacks where selected leaders’ identities remain unknown to others until they proposes transactions. Then, we present specific multi-leader BFT constructions that support a pipelined processing methods, realizing high processing parallelism and optimized throughput. Besides, a cross-leader view-change mechanism is designed for multi-leader BFT to enable efficient replacement of malicious leaders. Furthermore, we analyze the impact of the number of leaders on security and demonstrate that our proposals meet the required security standards. Experimental results reveal that the system achieves a throughput of up to 101 ktx/sec with 128 nodes, highlighting the potential of multi-leader BFT to significantly enhance the performance of blockchain systems.

Federated Learning (FL) enables collaborative model training across distributed devices while preserving data privacy. However, it faces critical security challenges, including centralization risks and poisoning attacks, which degrade robustness and scalability. Existing schemes struggle to simultaneously mitigate targeted and untargeted poisoning attacks, impose restrictive adversary ratio assumptions (poison ratio < 50%), and suffer from privacy-performance trade-offs. To address these limitations, we propose SharBipole, a decentralized FL scheme integrating sharding blockchain with a novel dual-metric defense mechanism, Bipole. SharBipole employs a Byzantine Fault Tolerant-enabled sharding architecture to eliminate single points of failure, reduce communication overhead, and enable parallel model aggregation. Meanwhile, the Bipole module defends against poisoning attacks using two adaptive similarity metrics to filter malicious updates dynamically. Reinforcement learning optimizes threshold adjustments, while noise-aware adaptive clipping balances privacy and model utility. Further, we give convergence analysis to prove the theoretical soundness and scalability of SharBipole. Lastly, extensive experimental evaluations demonstrate that SharBipole supports poison ratios exceeding 50% and improves throughput and latency. The model replacement attack with 60% adversaries is entirely ineffective against SharBipole, and the label-flipping attack achieves an attack success rate of only 2.344%. SharBipole establishes a scalable, secure, and privacy-preserving solution for distributed learning in massive environments.

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 relayers' identity and contribution during transaction propagation. We employ a free pricing mechanism based on the game between relayers 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.

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 crosschain gas overhead. Moreover, existing low-overhead crosschain mechanisms, particularly hashed time-locked contract (HTLC)-based schemes, inherently rely on pairwise locking and thus cannot efficiently support one-to-many settlement. We propose an interchain 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 crosschain 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 crosschain 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.

Blockchain-based information storage and retrieval systems face significant challenges in achieving efficiency, privacy, and auditability when operating across heterogeneous blockchain platforms. Existing solutions often struggle to balance these requirements, particularly in cross-chain environments involving both public and consortium blockchains. This paper proposes a novel framework that leverages cross-chain technology to address these limitations. The framework integrates multi-party threshold cross-chain consensus to optimize verification efficiency and reduce the computational burden on trusted nodes. To ensure privacy-preserving information querying and retrieval, advanced cryptographic techniques are employed. Additionally, a dedicated auditor set within the consortium blockchain is introduced to detect malicious behavior and enforce regulatory compliance. Comparative evaluations demonstrate that the proposed framework outperforms existing methods in terms of privacy protection, efficiency, and auditability. Experimental results on Hyperledger Fabric demonstrate significant improvements in throughput, achieving at least 20 Transactions Per Second (TPS), along with latency below 3.5 seconds and 300MB memory utilization under standard PC configurations. These findings validate the framework's practical viability for secure and efficient cross-chain information retrieval while maintaining superior performance compared to existing solutions.

Verifiable computation is essential for ensuring correctness in decentralized systems, yet existing approaches rely heavily on circuit-based proofs, task decomposition, or trusted hardware, which introduce high overhead and limit generality. To address these challenges, we propose CleVer, a compute-and-leave anonymous verification framework for general-purpose computation.CleVer avoids circuit-based proof generation by using snapshot-based state transitions, enabling single-step dispute resolution without task decomposition. We design a cumulative staking incentive mechanism that guarantees profitability for honest verifiers and enforces bounded finality under adversarial budgets. Furthermore, we introduce an anonymous verifier protocol to prevent targeted attacks and collusion. Security is analyzed under a formal threat model, and experiments demonstrate that CleVer significantly reduces verification rounds and on-chain burden compared with existing optimistic-verification frameworks. Our results show that CleVer provides an efficient, incentive-aligned, and privacy-preserving foundation for scalable off-chain computation.

Agents increasingly access large language models (LLMs) through API routers. A router terminates the client's transport-layer security session and opens a separate upstream session, so it holds the full interaction in plaintext. This makes the router an application-layer man-in-the-middle: it can rewrite agent tool calls, swap dependencies for typosquatted packages, trigger attacks only under audit-evading conditions, and passively exfiltrate secrets. Existing client-side defenses are evadable. We propose AEGIS, a provider-transparent attested API router whose data path is a client-verified faithful passthrough. AEGISconfines plaintext handling to a small hardware-enclave component while leaving authentication, scheduling, accounting, and management on the untrusted host. The client verifies the enclave before releasing plaintext. The host can neither read nor alter the interaction, and plaintext leaves only toward destinations fixed by the measured image. We show that all four malicious-router attack classes succeed against a plaintext-access baseline and are blocked by AEGIS, including adaptive tests against the same boundary. The trusted path is $851$ lines, carries three provider-native APIs without conversion, and completes every request under real-provider workload and concurrency. In a seeded audit pilot, two commodity coding agents find eight and ten of ten planted invariant violations. The local relay overhead is about six milliseconds per request.

Anonymous communication networks are critical infrastructures for protecting user privacy and defending against network surveillance. However, existing anonymous communication networks lack sufficient randomness in path selection, making them susceptible to adversarial prediction and manipulation. Inadequate identity protection further exposes nodes to targeted attacks. Moreover, purely random circuit selection can compromise fairness in long-term node incentives, reducing nodes’ motivation for participation. To address these issues, this paper proposes FRanCS, a blockchain-based Fair and Randomized anonymous circuit selection scheme. Specifically, the scheme employs rolling hash functions and zero-knowledge proofs (ZKPs) to achieve dynamic anonymity in node identity management. At the same time, an exponential key-value random sampling algorithm is designed to simultaneously ensure unpredictability in path selection and fairness in long-term node incentives. Theoretical analysis and experimental results demonstrate that the proposed scheme effectively resists path prediction attacks and node identity exposure risks, achieves fairness in long-term incentives, and exhibits robust security and efficiency in practical deployment scenarios.

Imagine a group of users wishing to perform offline micropayments (e.g., splitting the daily dinner bills) without revealing who paid what or relying on a central coordinator. Existing blockchain Payment Channel Networks (PCNs) fall short in such scenarios: they are designed for pairwise payments instead of multi-party settings, leak transaction metadata, and struggle to ensure consistent channel state under adversarial conditions. We present MystChannel, a privacy-preserving blockchain multi-party payment channel protocol. MystChannel introduces Blind Ring Adaptor Signatures (BRAS) to achieve transaction unlinkability and value privacy, independent of fixed payment amounts. To further ensure robustness against malicious par ticipants, we integrate an aggregable multi-signature scheme with a pipelined multi-leader Byzantine Fault Tolerant consensus protocol, enabling concurrent transaction processing and con sistent state agreement. MystChannel is provably secure under the Universal Composability (UC) framework. We implement and evaluate MystChannel, demonstrating that it achieves a 59.77× speedup over Bitcoin on-chain payments and is 77.375× faster than the state-of-the-art privacy-preserving multi-party channel AMHL [NDSS’19]. Our results affirm MystChannel as an efficient and secure off-chain payment infrastructure.

In Ethereum, DevP2P is the fundamental network-layer protocol set that supports consensus mechanisms, transaction propagation and smart contract execution. Due to the importance of DevP2P, its bugs can be exploited by the attacker to cause security problems like denial of service, leading to property loss on Ethereum. However, existing blockchain testing approaches focus on the bug detection of consensus and application layers, causing many serious DevP2P bugs to be missed. In fact, detecting DevP2P bugs has some key challenges, including how to generate effective inputs and how to detect complex bugs. This paper designs D2PFuzz, the first network-layer differential fuzzing approach of bug detection for Ethereum. It consists of two key techniques: (1) a query-based fuzzing strategy that dynamically generates valid DevP2P messages according to network, chain and node state changes; and (2) a multi-node differential checking method that identifies important differences of DevP2P response messages from multiple nodes in the same blockchain to detect semantic bugs. We have evaluated D2PFuzz on five open-source and popular Ethereum node implementations, including Geth, Erigon, Reth, Besu and Nethermind. D2PFuzz in total finds 15 unique bugs, 12 of which are previously unknown. Compared to two state-of-the-art blockchain testing approaches including LOKI and Hive, D2PFuzz improves testing coverage by 3.7x and 21.6x, respectively, and finds 13 bugs missed by these approaches.