Decentralized identity (DID) is a key infrastructure for Web3, granting users sovereign control over their private identity data. While existing DID systems like FADID-TT (WWW'25) realize anonymity and traceability within a single domain, the Web3 ecosystem is a multiverse of independent domains like DeFi, GameFi, and DAO. This multi-domain reality presents critical issues for current DID solutions. First, most existing solutions are built on the monolithic committee architecture, facing severe scalability bottlenecks as the committee size grows. Second, most existing solutions cannot offer strong cross-domain anonymity, where frequent cross-domain interaction inevitably exposes the user's privacy. Third, existing methods for tracing the identities of malicious users are inefficient.

Privacy-preserving data trading allows data owners to sell data to consumers through a data trading web platform, the data market, without disclosing sensitive information in raw data. It enables legitimate data transmission and aggregation, facilitating large-scale data-driven model training. However, existing differential privacy-based approaches struggle to inject precisely calibrated noise in a trustworthy manner without revealing raw data to a third party, thus making them fail in achieving strong fairness and controllable privacy simultaneously, especially when facing malicious external adversaries or a corrupted data market.

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.

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.

Automatic Dependent Surveillance–Broadcast (ADS-B) is widely deployed in both civil and unmanned aviation networks, yet its plaintext broadcast design leaves it vulnerable to eavesdropping, spoofing, and message forgery. Existing cryptographic solutions incur excessive overhead, depend on complex key infrastructures, and fail to accommodate the broadcast nature of ADS-B, making them unsuitable for real-time IoT aviation scenarios. In this paper, we propose ADSB-IBBE, the first lightweight and scalable security scheme tailored for confidential ADS-B broadcast communication in IoT-enabled aerial networks. ADSB-IBBE integrates a novel Identity-based Broadcast Encryption (IBBE) construction to enable efficient key distribution, together with a purpose-built, format-preserving stream cipher that secures critical ADS-B fields without extending the message size. It further incorporates a customized sharding consortium blockchain for decentralized identity management and rapid key revocation, while a header compression mechanism reduces transmission overhead. Security analysis confirms Indistinguishability against Selective-Identity Chosen Ciphertext Attacks (IND-sID-CCA) security under the Random Oracle Model (ROM). Experiments show over 80% lower computational cost and 90% reduced communication overhead compared to the most demanding baseline, with increasing advantages as the number of receivers grows, achieving the lowest overhead of 510bit header and 80bit ciphertext. These results demonstrate the suitability of our scheme for secure and efficient ADS-B communication in next-generation IoT-enabled air traffic management (ATM) systems.

Data has become a critical driver of innovation in artificial intelligence and the evolution of 6G technologies. The explosive growth of data volume accelerates the convergence of cloud and edge computing, while simultaneously posing heightened challenges to data security and privacy. The emerging cloud-edge-device collaborative paradigm enables dynamic and large-scale data circulation across heterogeneous entities, exposing systems to complex threats such as malicious edge nodes and eavesdropping over untrusted communication channels. In response to these issues, we propose EdgeGuard, a secure and decentralized framework for cloud-edge-device data circulation. EdgeGuard is specifically designed for highly dynamic environments and ensures robust data confidentiality, integrity, traceability, and resilience against both malicious external attackers and compromised edge servers. To underpin its cryptographic foundation, we develop two core primitives. Specifically, we introduce an Aggregatable Publicly Verifiable Secret Sharing (APVSS) scheme that enables efficient sharing of field elements while supporting aggregation and public verifiability. Furthermore, we construct AggDKG, a distributed key generation (DKG) protocol. AggDKG achieves public verifiability and bias resistance with an expected total communication cost of${\mathcal {O}}(\kappa n^{3})$effectively overcoming the scalability limitations inherent in traditional complaint-based protocols. Collectively, these components form a comprehensive framework that strengthens secure and efficient data circulation in cloud-edge-device systems. Experimental data show that AggDKG delivers clear performance gains: across all tested scales, its total running time is only about 8%–65% of that of the DKG of Gurkan et al., and at$n=256$, it reduces per-node runtime by approximately 27% compared with the DKG of Gennaro et al. These results highlight EdgeGuard's superior scalability, lower latency, and stronger Byzantine resilience for secure large-scale deployments.

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.

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 sharding is a promising approach to enhancing scalability by partitioning the network into smaller, parallel shards. However, existing sharding blockchains that rely on Byzantine fault tolerance protocols require large shard sizes to meet strict security thresholds, limiting scalability, while relaxing security parameters can lead to liveness and safety violations. In this work, we present Camael, a secure sharding blockchain that achieves corrupted-shard tolerance through effective detection and processing mechanisms for both liveness and safety violations. Specifically, fake liveness violations forged by malicious nodes are accurately detected via a two-phase reporting and confirmation mechanism, while concealed safety violations are efficiently identified using a lightweight snapshot mechanism. Furthermore, a state determination process ensures overall system consistency. Malicious nodes are precisely identified through a conviction mechanism, which enables the replacement of the targeted nodes and the reconfiguration of the shards. Notably, Camael ensures security while preserving a global fault tolerance of 1/3 and tolerating corrupted shards, with each shard accommodating up to 2/3 malicious nodes. Extensive experiments conducted on 2000 AWS EC2 nodes across 4 regions demonstrate that Camael improves throughput by 3.56 times compared to the baseline (Kronos, NDSS'25), achieving a throughput of 109.3 ktx/sec, while the violation processing requires only 1.64 sec.

Tubular structure segmentation (TSS) is important for various applications, such as hemodynamic analysis and route navigation. Despite significant progress in TSS, domain shifts remain a major challenge, leading to performance degradation in unseen target domains. Unlike other segmentation tasks, TSS is more sensitive to domain shifts, as changes in topological structures can compromise segmentation integrity, and variations in local features distinguishing foreground from background (e.g., texture and contrast) may further disrupt topological continuity. To address these challenges, we propose Topology-enhanced Test-Time Adaptation (TopoTTA), the first test-time adaptation framework designed specifically for TSS. TopoTTA consists of two stages: Stage 1 adapts models to cross-domain topological discrepancies using the proposed Topological Meta Difference Convolutions (TopoMDCs), which enhance topological representation without altering pre-trained parameters; Stage 2 improves topological continuity by a novel Topology Hard sample Generation (TopoHG) strategy and prediction alignment on hard samples with pseudo-labels in the generated pseudo-break regions. Extensive experiments across four scenarios and ten datasets demonstrate TopoTTA's effectiveness in handling topological distribution shifts, achieving an average improvement of 31.81% in clDice. TopoTTA also serves as a plug-and-play TTA solution for CNN-based TSS models.

This work proposes a multi-level compiler framework to transform programs with loop structures to efficient algorithms over fully homomorphic encryption (FHE). We observe that, when loops operate over ciphertexts, it becomes extremely challenging to effectively interpret the control structures within the loop and construct operator cost models for the main body of the loop. Consequently, most existing compiler frameworks have inadequate support for programs involving non-trivial loops, undermining the expressiveness of programming over FHE. To achieve both efficient and general program execution over FHE, we propose CHLOE, a new compiler framework with multi-level control-flow analysis for the effective optimization of compound repetition control structures. We observe that loops over FHE can be classified into two categories depending on whether the loop condition is encrypted, namely, the transparent loops and the oblivious loops. For transparent loops, we can directly inspect the control structures and build operator cost models to apply FHE-specific loop segmentation and vectorization in a fine-grained manner. Meanwhile, for oblivious loops, we derive closed-form expressions and static analysis techniques to reduce the number of potential loop paths and conditional branches. In the experiment, we show that CHLOE can compile programs with complex loop structures into efficient executable codes over FHE, where the performance improvement ranges from 1.5× to 54× (up to 105× for programs containing oblivious loops) when compared to programs produced by the-state-of-the-art FHE compilers.

Federated Learning (FL) is a distributed machine learning paradigm that allows multiple clients to train models collaboratively without sharing local data. Numerous works have explored security and privacy protection in FL, as well as its integration with blockchain technology. However, existing FL works still face critical issues. i) It is difficult to achieving poisoning robustness and data privacy while ensuring high model accuracy. Malicious clients can launch poisoning attacks that degrade the global model. Besides, aggregators can infer private data from the gradients, causing privacy leakages. Existing privacy-preserving poisoning defense FL solutions suffer from decreased model accuracy and high computational overhead. ii) Blockchain-assisted FL records iterative gradient updates on-chain to prevent model tampering, yet existing schemes are not compatible with practical blockchains and incur high costs for maintaining the gradients on-chain. Besides, incentives are overlooked, where unfair reward distribution hinders the sustainable development of the FL community. In this work, we propose FLock, a robust and privacy-preserving FL scheme based on practical blockchain state channels. First, we propose a lightweight secure Multi-party Computation (MPC)-friendly robust aggregation method through quantization, median, and Hamming distance, which could resist poisoning attacks against up to <50% malicious clients. Besides, we propose communication-efficient Shamir's secret sharing-based MPC protocols to protect data privacy with high model accuracy. Second, we utilize blockchain off-chain state channels to achieve immutable model records and incentive distribution. FLock achieves cost-effective compatibility with practical cryptocurrency platforms, e.g. Ethereum, along with fair incentives, by merging the secure aggregation into a multi-party state channel. In addition, a pipelined Byzantine Fault-Tolerant (BFT) consensus is integrated where each aggregator can reconstruct the final aggregated results. Lastly, we implement FLock and the evaluation results demonstrate that FLock enhances robustness and privacy, while maintaining efficiency and high model accuracy. Even with 25 aggregators and 100 clients, FLock can complete one secure aggregation for ResNet in 2 minutes over a WAN. FLock successfully implements secure aggregation with such a large number of aggregators, thereby enhancing the fault tolerance of the aggregation.

As the artificial intelligence, large model, Metaverse, and Web 3.0 develop rapidly, data is being traded constantly. Existing data exchange methods primarily rely on trusted third parties, which compromises fairness and decentralization. Moreover, existing methods often overlook data access control during trading and typically employ an one-to-one model, resulting in high communication and computational overhead. To address these issues, this article makes the following contributions. First, we propose a blockchain-based secure and fair data trading scheme named fair data trading (FairDT). By leveraging blockchain and smart contracts, FairDT achieves decentralized data trading with high throughput and scalability. Second, we design a fair data exchange mechanism that utilizes commitment schemes, Merkle trees, and other techniques to facilitate dispute resolution with constant on-chain cost when conflicts arise. Third, we incorporate attribute-based encryption to enable fine-grained access control in data trading, thereby reducing the computational burden on data sellers. Finally, we prove that FairDT satisfies access control, fair exchange, completeness, and termination properties. Experimental results on the Ethereum testnet demonstrate that the on-chain cost remains constant, showing that FairDT is highly efficient.

The automatic dependent surveillance broadcast (ADS-B) system is a critical surveillance technology in air traffic management (ATM), essential for enhancing aviation safety and operational efficiency. However, ADS-B broadcasts plaintext messages over open channels without authentication mechanisms, and is constrained by message length limitations and low bandwidth, making it susceptible to various attacks, including deception, tampering, and replay. To address these challenges, we propose a secure and lightweight blockchain-integrated certificateless signature scheme (ECB-CLS) tailored for ADS-B systems with packet resilience. Specifically, we introduce an efficient signature verification algorithm based on elliptic curve cryptography (ECC) that supports batch verification without the need for certificate management, complex bilinear pairing, or hash-to-point calculations, significantly reducing computational overhead. Furthermore, our scheme leverages blockchain to ensure the decentralization and traceability of massive public keys and provides provable security against Type I and Type II adversary attacks. To address packet loss in practical environments, we incorporate both standard and enhanced Reed-Solomon (RS) coding to recover lost data. Experimental evaluations demonstrate that our blockchain-integrated ECB-CLS scheme offers substantial advantages in computational efficiency and signature length compared to existing methods, while also showing that RS coding introduces low-performance overhead. This makes our solution highly suitable for resource-constrained ADS-B systems.

The AI-driven Internet of Things (AIoT) has been widely applied in the field of Internet of Vehicles (IoV) for vehicular cooperation. Federated learning (FL), due to its ability to protect users’ data privacy, reduce communication overhead, and facilitate real-time decision making, is widely applied in the augmented intelligence of things for vehicles (AIoV). However, integrating FL with AIoV poses challenges, including the absence of fine-grained access control, insufficient safeguards for FL tasks and vehicle identities, inadequate security for data transmission, and shortcomings in protecting data storage. These vulnerabilities may lead to risks such as vehicle tracking, model information theft, and data tampering. To address these challenges, we propose a privacy preservation mechanism for AIoV via cloud–edge–vehicle hierarchical sharding blockchain. First, we propose a hierarchical anonymous authentication scheme for IoV devices with stronger scalability and higher fault tolerance. Vehicles only know the attributes of each other or which shard they belong to. Second, we present a secure FL task assignment scheme for AIoV. Edge nodes utilize attribute-based encryption to deploy fine-grained FL tasks based on vehicle attributes. Only users who meet the attributes can decrypt the content, protecting FL tasks content and participant identities. Third, we present a secure data transmission scheme between AIoV devices to protect the identity and data privacy of both parties, while also achieving noninteractive key agreement. Additionally, we propose a scalable secure data sharing and storage scheme based on hierarchical sharding blockchain, aiming to reduce storage overhead and minimize trust costs.

Blockchain technology has experienced substantial development and has found extensive applications in the Internet of Things (IoT), which facilitates decentralized communications between devices. Blockchain enables individuals to record transactions, store data, and exchange value within a distributed ledger. However, with the development of blockchain, the performance bottleneck caused by scalability issues has become increasingly prominent. The sharding technique presents an effective solution to the scalability problem of blockchain systems by partitioning a complex blockchain network into multiple smaller node clusters. Each cluster independently maintains a ledger, reducing complexity and enhancing system efficiency. Besides, payment channels allow users to interact off-chain and rely on the security of the main chain for final settlement, accelerating the processing of numerous small transactions. However, existing sharding techniques face challenges with crossshard operations, including high conflict rates and inefficiencies in handling multiple small off-chain transactions. In this work, we propose a tree-based sharding protocol and by designing an ordering mechanism, our protocol could effectively resist front-running attacks. Furthermore, based on the proposed treebased sharding protocol, a cross-shard virtual channel protocol is designed and implemented for high-frequency cross-shard transactions. Finally, we implement a prototype for our protocol in Tendermint, which achieves 6700+ transaction throughput and lower confirmation latency both in intra-shard and cross-shard transactions with 16 shards compared to existing works, and we measure the cost of each phase of the cross-shard virtual channel protocol which takes approximately 250ms to process 300 transactions.

With the approach of the quantum era, public key cryptography (PKC) faces risks, which also presents challenges to blockchain technologies that utilize PKC as a core component. Sharding blockchain is a promising way to realize scalability, yet current research does not consider quantum-resistant sharding blockchains as it is non-trivial to design cross-shard communication and transaction processing method without PKC. Besides, blockchain enables reliability in data transmission and unbreakable communication while current schemes suffer from high overhead and low throughput. In this paper, we propose a quantum-resistant sharding blockchain (QRShar) and a secure data transmission scheme (QRDT) to fill the above gap. Firstly, we design a secure and efficient cross-shard communication pattern utilizing hash-based message authentication code (HMAC) and erasure code to reduce the transmission load and achieve high efficiency. Secondly, we propose the a quantum-resistant sharding blockchain utilizing optimized cross-shard transaction processing method to decrease the consensus execution frequency. Thirdly, we introduce a quantum-resistant key agreement protocol through the verifiable secret sharing on cryptographic hash function and we also offer a data transmission scheme to realize efficient QRDT. Furthermore, we conduct security analysis and performance evaluations for our schemes. The results show that the QRShar throughput can reach up to 34 KTPS and the latency stays below 2 seconds. The key agreement latency is just 43ms.

In the era of digital society, data has emerged as both the cornerstone of computational social systems and the key enabler for social value creation. The development of trustworthy data trading paradigms plays a pivotal role in facilitating data-driven social analysis and collective decision-making processes. Since data can be easily copied, transmitted, and tampered with, data trading has problems such as difficulties in confirming rights, tracing sources, and monitoring. Existing data trading schemes utilize blockchain to trace trading process, decentralized identifier (DID) to achieve autonomous control of user identity, and verifiable credential (VC) to determine data rights. However, existing schemes still have the following problems. First, most of schemes are targeted at small-scale data. Second, the possibility of user identity fraud and abuse still exists. Third, it is difficult to coordinate between VC and data trading. Fourth, existing schemes text colored lack supervision mechanisms. To solve these issues, first, we design a secure data trading (SDT) scheme. The scheme separates data and data rights, which provides a new idea for efficient and reliable trading of larger-scale enterprise data. Second, we design an identity control mechanism. The mechanism effectively improves the credibility of user identity through real-name verification. Third, we design a secure data rights circulation mechanism. The mechanism can quickly determine the rights of trading data and carry out automated verification, which solves the problem of coordination between credential and data trading. Fourth, we design a trading supervision mechanism. The mechanism stores behavioral data on blockchain, which achieves effective rights maintenance of data trading. Finally, based on product carbon footprint scenario, the designed data trading scheme is simulated and verified. The results show that it is secure and feasible to use the data trading scheme proposed for data trading, and it can be used to significantly enhance the quality and reliability of social computing research.

Addressing blockchain's insufficient throughput and scalability is imperative for practical viability. Off-chain approaches, such as state channels (including Hash Time Lock Contract (HTLC), virtual channels), demonstrate enhanced throughput by enabling parallel transaction processing. While virtual channels introduce execution complexity, HTLC suffers from high update delays. Moreover, existing methods face network attacks. We present Sharding-based Hybrid State Channel (SharHSC) to address these issues. First, we introduce a novel off-chain sharding architecture, which partitions proxy nodes into multiple shards. Thus, when the off-chain node count increases, adding shards enhances system throughput. Second, each shard establishes a supervisory committee to record latest channel statuses to ensure accurate fund distribution upon channel closure. Third, we combine the strengths of HTLC and virtual channels. In particular, SharHSC constructs a single virtual channel across all the nodes involved in the payment by treating the nodes between payer and payee as an intermediate entity, which utilizes HTLC for fund routing. This realizes both low latency and streamlined complexity. Finally, our work is substantiated by security analysis and experiments. As the node number varies, compared with HTLC and virtual channels, the latency is reduced by 49.32% and 31.82%, and the throughput is increased by 8.93 and 1.89 times.

In Web 3.0’s pursuit of a decentralized and user-autonomous network, traditional access control methods, such as central servers and weak decentralized algorithms, are insufficient regarding security, fault tolerance ability, and scalability. To solve this, we first design a decentralized multi-committee attribute-based encryption, X-ABE, to address the weak decentralization and low fault tolerance in Multi-Authority Attribute-Based Encryption (MA-ABE). X-ABE replaces MA-ABE’s fragile attribute authorities with robust attribute committees, each composed of multiple nodes. By developing dual-wrapped shares techniques, we address the increased dimensionality challenge of secret sharing while maintaining only 1 distributed key generation instance. Also, a formal security definition and proof under the partial adaptive model are given using dual system encryption. Second, X-LOCK, an X-ABE based decentralized access control utilizing consensus plus sharding, is proposed for Web 3.0, to achieve full decentralization, consistency, fault tolerance, user autonomy, and scalability. Third, X-ABE-R is proposed for attribute revocation and is demonstrated in X-LOCK-R with sharding blockchain as an immutable revocation ledger. Fourth, a formal definition and comparative analysis of X-ABE’s fault tolerance abilities are demonstrated, covering aspects of liveness and safety, along with the complexity analysis. Fifth, practical evaluations are conducted, demonstrating that while improving fault tolerance, the overhead remains acceptable.

Cryptocurrencies and permissionless blockchains allow nodes from all over the world to join, and their rapid development has created enormous blockchain networks with nodes spanning the globe. Blockchain network partitioning attacks split the network into separate node groups through disrupting communication, causing information inconsistency, and facilitating malicious behaviors like double-spending and selfish mining, threatening the blockchain security. Existing research primarily studies concrete partitioning attack methods. However, it is hard to analyze practical post-attack security and efficiency impacts on blockchains and design effective countermeasures. This paper studies practical network partitioning attacks’ impacts on existing proof-of-work-based (Bitcoin) and proof-of-stake-based (Ethereum) permissionless blockchains. We theoretically analyze and experimentally confirm the adverse effects of network partitioning on blockchain performance and security. Network partitioning will cause blockchain throughput to plummet, and cause block generation delay to increase rapidly. In our experiments on Ethereum 2.0, when the bandwidth between the partitioned networks is lower than 768 Kbps, the throughput begins to plummet precipitously until it ultimately falls to 0. What’s worse, network partitioning will significantly increase the success rate of double-spending. In our experiments on Bitcoin, when the bandwidth between the partitioned networks is less than 256 Kbps, the success rate of double-spending reaches 50%. To solve the above issues, we propose countermeasures leveraging a freezing threshold to safeguard the security of permissionless blockchains and resist double-spending attacks. We experimentally validate that the countermeasures enhance the resistance of permissionless blockchains to network partitioning attacks. It reduces the probability of double-spending in partitioned networks, thereby ensuring security and reliability.

Blockchain and cryptocurrencies are developing rapidly, and the scalability issue has become a constraint on their practical application and development. Off-chain payment channel is an effective solution to the scalability problem of blockchain. Currently, various payment channel protocols have been proposed. However, privacy issues are vital in payment channels. Existing works that consider privacy issues mainly focus on payment channel networks and payment channel hubs, while there is little work on two-party and multi-party channels. In addition, many existing payment channel works that consider privacy protection fix the transaction amounts to ensure the hiding of payment relationships or rely on smart contracts, which will hinder the practical application of payment channels. In this work, we propose a two-party privacy-preserving payment channel protocol that is compatible with Bitcoin (TBPChannel), achieving value privacy and unlinkability, while supporting variable transaction amounts. On this basis, we propose a privacy-preserving multi-party payment channel protocol (MBPChannel), which removes the role of untrusted operators in previous multi-party settings and further achieves robustness. We formally model the protocols in the universal composability framework and prove the security. Finally, we implement the protocols and provide a performance evaluation. The results demonstrate the scalability and practicality of our protocols. Compared to current protocols, even though we use privacy-preserving methods, our protocols are still efficient and applicable in practice.

The One-Tap Authentication (OTAuth) service enables users to quickly log in or sign up for app accounts using their phone number. OTAuth provides a more secure and convenient alternative to password-based and Short Message Service (SMS)-based authentication schemes. Consequently, the OTAuth service has been adopted by numerous Mobile Network Operators (MNOs) worldwide. However, a high severity vulnerability remains unaddressed in the OTAuth service, which allows an attacker to access a victim’s various app accounts, posing a significant risk to user privacy and data security. In this paper, we present LoadShow, which, to the best of our knowledge, is the first security-enhanced OTAuth scheme to address this vulnerability. We propose a novel dynamic application identification technique that aims to address the root cause of this vulnerability, i.e., the inability of MNOs to distinguish between different applications on the same device. Specifically, application identification is based on the hardware load side-channel and captures the unique CPU and GPU load characteristics of applications through the sequence of timing values of fingerprinting functions. We evaluate the effectiveness of LoadShow by accuracy, False Positive Rate (FPR), and True Positive Rate (TPR). We also evaluate its multi-platform compatibility on devices with different architectures and models. LoadShow achieves over 90% accuracy, with a TPR exceeding 90% and an FPR below 1%. The evaluation results demonstrate LoadShow’s capability to effectively differentiate between applications on a device, defend against app impersonation attacks, and reliably identify legitimate applications.

Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against presentation attacks. With ad-vancements in sensor manufacture and multi-modal learning techniques, many multi-modal FAS approaches have emerged. However, they face challenges in generalizing to unseen attacks and deployment conditions. These chal-lenges arise from (1) modality unreliability, where some modality sensors like depth and infrared undergo signifi-cant domain shifts in varying environments, leading to the spread of unreliable information during cross-modal feature fusion, and (2) modality imbalance, where training overly relies on a dominant modality hinders the conver-gence of others, reducing effectiveness against attack types that are indistinguishable by sorely using the dominant modality. To address modality unreliability, we propose the Uncertainty-Guided Cross-Adapter (U-Adapter) to recognize unreliably detected regions within each modality and suppress the impact of unreliable regions on other modal-ities. For modality imbalance, we propose a Rebalanced Modality Gradient Modulation (ReGrad) strategy to rebal-ance the convergence speed of all modalities by adaptively adjusting their gradients. Besides, we provide the first large-scale benchmark for evaluating multi-modal FAS per-formance under domain generalization scenarios. Exten-sive experiments demonstrate that our method outperforms state-of-the-art methods. Source codes and protocols are released on https://github.com/OMGGGGG/mmdg.

Web3 is a revolutionary Internet paradigm that focusing decentralization, user empowerment, and intelligence. One of its key technologies is decentralized identity (DID), which has gained significant attention recently. However, existing DID solutions are not scalable enough to be compatible with the large-scale identity node applications required by Web3 across various fields. To overcome this challenge, we propose the first multi-layer Web3 DID architecture utilizing sharding blockchain, which provides management, scalability, and compatibility. This architecture leverages leader shards and the main chain to establish trust, while regular shards manage DID-related transactions. Specific system processes and query optimizations are also given. Besides, formal security analysis and comprehensive simulation evaluations have demonstrated that the architecture can achieve all proposed security and performance goals, including low latency of down to 2 seconds and high throughput of up to 90KTPS.

The inherent massive heterogeneous devices and open channels in the Internet of Things (IoT) present significant challenges for identity authentication between devices and cloud servers. For this issue, reliable protocols ensure the legality of participants and act as a crucial method to provide security for authentication. In previous research, schemes devised by researchers exhibit certain security vulnerabilities, making it challenging to withstand comprehensive network attacks, e.g., stolen device attacks, replay attacks, impersonation, etc. Additionally, some protocols have complex interaction processes, which incur significant computational redundancy and resource loss. Motivated by this, this article proposes an anonymous and certificateless lightweight authentication protocol (ACLAP) for device-to-server and device-to-device based on elliptic curve cryptography. It improves the communication quality between devices and cloud servers and solves the security risks in authentication. In the scheme, we utilize device users’ passwords and biometric features as verification credentials without storing any trusted proofs on the cloud server. We address the issue of resource consumption caused by numerous devices in the IoT environment. From formal security analysis and comparisons with other works, our protocol has preferable security performance and effectively saves communication resources for authentication. Simulation results demonstrate the feasibility and practical significance of the scheme.

The cloud-edge-end architecture is suitable for many essential scenarios, such as 5 G, the Internet of Things (IoT), and mobile edge computing. Under this architecture, cross-domain and cross-layer data sharing is commonly in need. Considering cross-domain data sharing under the zero-trust model, where each entity does not trust the others, existing solutions have certain problems regarding security, fairness, scalability, and efficiency. Aiming at solving these issues, we conduct the following research. First, a new plaintext checkable encryption scheme is constructed, which can be used on lightweight IoT devices to verify the ciphertext validity sent by a data owner. Second, we propose a new multi-domain cloud-edge-end architecture based on sharding blockchains and design a cross-domain data sharing scheme under the partial trust model to achieve security, scalability, and high performance. Third, a cross-domain data sharing scheme under the zero trust model is further designed, which can ensure the fairness of both parties in data sharing. Fourth, we give a formal security definition and analysis of cross-domain data sharing. Fifth, we conduct a detailed theoretical analysis of the protocol and give an in-depth functional test and performance test, including the throughput and latency of data sharing policy registration and execution.

Due to the promising scalability property, sharding technology has gained widespread attention. It improves the transaction throughput of blockchain systems but also introduces cross-shard transactions. Current two-phase commit (2PC) protocols process different cross-shard transactions sequentially, resulting in significant system overhead and low throughput. Besides, current sharding blockchains rely on Byzantine fault tolerance (BFT) as a black box, lacking specific designs to efficiently handle cross-shard proposals. Moreover, cross-shard communication complexity is high, and transaction processing parallelism is low. In this paper, we first propose P-2PC, a general framework to process cross-shard transactions of different phases in a pipelined way, suitable for most sharding blockchains. Further, we design Cherubim with improved quadruple 2PC, 4P-2PC. By combining P-2PC with an intra-shard pipelined BFT, 4P-2PC achieves both intra-shard and cross-shard pipelined processing. Combined with a newly designed batch processing method, each shard processes 4 transaction batches simultaneously through 1 round of calculation and communication, compared to 4 rounds in previous work. In particular, Cherubim seamlessly integrates a multi-signature algorithm supporting further aggregation, reducing communication complexity. Furthermore, we evaluate our work through theoretical analysis and implementation, proving that Cherubim has a communication complexity linear to the node number. We also propose horizontal and vertical consensus parallelism degrees to evaluate the parallelism ability. Compared to the state-of-the-art solutions, the evaluation demonstrates that Cherubim achieves a transaction throughput improvement of at least 2.28×.

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.

Sharding technology is crucial to achieve decentralization, scalability, and security simultaneously. However, existing sharding blockchain schemes suffer from high cross-shard transaction processing latency, low parallelism, incomplete cross-shard views of shard members, centralized reconfiguration, high overhead of randomness generation, and lack of formalized protocol design and security proofs. This paper proposes a flexible sharding (FS) blockchain protocol. First, a cross-shard Byzantine fault tolerance (CSBFT) protocol is designed to cut down confirmation delays when processing cross-shard transactions. Second, we utilize multiple parallel CSBFT where each node acts not only as a leader but also as multiple ordinary members to break through the performance bottleneck caused by a leader’s bandwidth and computing power, improving the system parallelism. Third, a cross-shard transaction censorship attack is proposed, and a cross-shard view-change mechanism is designed to defend against it. Fourth, a secure and truly decentralized shard reconfiguration method combining proof-of-work, proof-of-possession, and intra-shard BFT is designed. Fifth, we utilize a formal protocol design method and give strict security proof for each protocol. Finally, we evaluate FS from both theoretical and practical perspectives. FS is proven to have lower communication and computation complexity and achieve considerable performance.

Sharding blockchains are proposed to solve the scalability problem while maintaining security and decentralization. However, there are still many issues to be solved. First, the member selection and assignment process are not strictly analyzed, which might lead to an increase in the adversary proportion. Second, current intra-shard consensus algorithms are inefficient. Besides, cross-shard transaction processing costs expensive system overhead. Moreover, there is a lack of a formal security framework. In this article, we propose a secure and scalable hybrid consensus (SSHC). First, we propose a fair sharding selection scheme to select committee members, including mining processes and member lists confirmation by a reference committee. Second, a pipelined Byzantine fault tolerance for intra-shard consensus is designed, combining the pipelined technology with threshold signatures. Third, we propose a responsive sharding transaction batch processing mechanism to handle cross-shard transactions, which reduces the number of calls to Byzantine fault tolerance algorithms. Fourth, a secure committee reconfiguration method is designed to update shard members efficiently. Furthermore, we employ a formal security framework to design and analyze a sharding blockchain. For an adversary whose computational power fraction is less than$1/3$, by reasonably setting a corruption parameter and other related parameters, SSHC is proved to achieve consistency and liveness.

In permissionless blockchains, due to the corruption attack of an adversary, nodes participating the protocol need to be updated regularly. In the process of node selection and committee reconfiguration, there may exist some problems. First, a complicated secure randomness generation protocol is in need. Besides, an adversary might obtain a mining puzzle in advance and start mining in ahead of honest nodes. Moreover, an adversary usually has an advantage of network delay. In order to solve the above problems, we conduct the following research. Firstly, we propose a PoW solution withhold attack against PoW-based member selection methods. An adversary might withhold his mining results in an epoch to obtain the mining puzzle of the next epoch in advance of honest nodes. Secondly, a secure shard reconfiguration protocol is designed, which does not rely on any complicated randomness generation protocol. Our shard reconfiguration protocol is proved rigorously to be secure, which means that in each selected committee, the honest node fraction exceeds a predefined target value. Thirdly, we implement our shard reconfiguration protocol. By carefully setting related system parameters, our protocol could be applied easily to most sharding blockchains. To our best knowledge, the shard reconfiguration protocol proposed in this paper is the first protocol that could safely implement node selection and committee reconfiguration of a sharding blockchain without using a secure randomness, which greatly reduces the communication and time overhead caused by the generation of a randomness.

Sensor nodes in wireless body-area networks are expected to use compressed sensing (CS) for ultralow-power consumption. For signal recovery in CS systems, compared with the software implementation, the hardware processor has superiority on power, real-time ability, and so on. Although orthogonal matching pursuit (OMP) is conventionally selected in hardware implementations, its low recovery performance is still a problem. We propose to use the subspace pursuit (SP) algorithm that can achieve higher recovery performance with less iteration to implement the hardware processor. First, a new SP by a square-root-free method and a computation reuse scheme are proposed to reduce the SP's complexity. Then, the fully paralleled architectures and elaborate pipelines are proposed to improve the processing throughput. Finally, we give an FPGA implementation prototype. Simulations compared with OMP show that the traditional SP can improve more than 8 dB on recovery performance or it can reduce more than 31% sampling paths in sensor nodes. These benefits are kept by our proposed SP because our improvement does not modify the algorithmic nature. Nevertheless, the significant complexity reduction is achieved by our proposal. The proposed SP processor can recover ECG signals with 28.2 dB and achieve the processing throughput of 44.4 K vectors/s.