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.

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.

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.

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.

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 recent years, Generative Adversarial Networks (GANs) have become essential tools in artificial intelligence research. Field Programmable Gate Arrays (FPGAs) offer remarkable flexibility, high performance, and energy efficiency for deploying GANs. However, the open and reprogrammable architecture of FPGAs, despite its advantages, introduces risks of unauthorized access and reverse engineering. To address this challenge, this paper presents a novel approach integrating Physical Unclonable Functions (PUFs) and logos to protect the Intellectual Property Rights (IPR) of GANs. Our method establishes a closed-loop conversion process where logos are transformed into PUF responses, generating unique identities fed into the GAN to reproduce the original logo. By embedding PUF response information into latent vectors, the generator produces images with embedded logos. Thanks to the uniqueness of PUF, a robust binding of the logo, FPGA, and GANs' IPR is implemented, allowing verification of the IPR with the assistance of a unique FPGA fingerprint, even when a publicly available logo is used. Experimental results show that embedding the logo does not change the performance of the original GANs, and the logo detection rate exceeds 90%. At the same time, the scheme can effectively resist brute force, fine-tuning and pruning attacks.

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.

The surge in image sharing on social media platforms escalates private information extraction for commercial use, increasing user demand for privacy protection. However, the dynamics of group communication within online social networks and the image compression imposed by platforms present significant challenges to secure key exchange and reliable image sharing in existing solutions. In this paper, we propose PrivSocial to prevent social media platforms from extracting private information in images shared within group communications. Specifically, we propose two frameworks, a server-based framework and a subscription-based framework, making PrivSocial applicable to different social media platforms and providing users with optional security levels, enhancing the flexibility and efficiency. To achieve intra-group key agreement and ensure image privacy protection, both frameworks integrate optimized continuous group key agreement and a novel image encryption scheme resisting compression. We implement an Android-based Priv-raster application and deploy a prototype on Twitter. Furthermore, we evaluate the proposed encryption scheme, and experimental results show that it has efficient encryption and decryption performance while being resistant to jigsaw puzzle solver attacks. The multi-user simulation experiments also demonstrate that the processing time of a single user is mere milliseconds, and the scheme can efficiently support tens of thousands of groups.

The space-air-ground integrated network (SAGIN) has a stringent demand on the efficiency of authentication protocols deployed in the devices that have been launched into the air and space. In this paper, we define the concept of the security model of conditional physical unclonable function (CPUF) that guarantees the security of the protocol while allowing the use of PUFs that can be modeled. We then propose a CPUF-based authentication and key agreement (AKA) scheme, named CPAKA, that addresses the challenges of device key leakage and inefficient authentication in resource-asymmetric environments. The CPAKA scheme embeds PUFs in weak nodes and deploys prediction models corresponding to the PUFs in strong nodes, eliminating the need to store challenge-response pairs or perform complex calculations. We formally prove the protocol's security under the decisional uniqueness assumption of CPUF and the universal composability framework, and we analyze its secrecy and authentication properties using the Tamarin prover. We also implement an Arbiter PUF on the ZYNQ-7020 FPGA, verify its accuracy through experiments, and show that CPAKA is secure, efficient, and suitable for SAGIN. Our CPAKA scheme greatly reduces computing and storage costs while improving authentication efficiency compared to traditional schemes.

The device fingerprinting technique extracts fingerprints based on the hardware characteristics of the device to identify the device. The primary goal of device fingerprinting is to accurately and uniquely identify a device, which requires the generated device fingerprints to have good stability to achieve long-term tracking of the target device. However, the fingerprints generated by some existing fingerprinting technologies are not stable enough or change frequently, making it impossible to track the target device for a long time. In this paper, we present FPHammer, a novel DRAM-based fingerprinting technique. The device fingerprint generated by our technique has high stability and can be used to track the device for a long time. We leverage the Rowhammer technique to repeatedly and quickly access a row in DRAM to get bit flips in its adjacent row. We then construct a physical fingerprint of the device based on the locations of the collected bit flips. The evaluation results of the uniqueness and reliability of the physical fingerprint show that it can be used to distinguish devices with the same hardware and software configuration. The experimental results on device identification demonstrate that the physical fingerprints engendered by our innovative technique are inherently linked to the entirety of the device rather than just the DRAM module. Even if the device modifies software-level parameters such as MAC address and IP address or even reinstalls the operating system, we can accurately identify the target device. This demonstrates that FPHammer can generate stable fingerprints that are not affected by software layer parameters.

With the continuous advancement of edge intelligence, edge servers undertake more and more intelligent computing tasks. Nowadays, there are a large number of IoT devices in the network in idle state. For instance, the mining process for consensus of miners in blockchain such as Bitcoin causes a waste of computing resources and energy. A natural question arises: can we couple the idle computing resources of network devices to continuously and credibly share the burden of edge intelligent computing tasks in a secure manner? The answer of this paper is yes. We propose a blockchain-based IoT resource monitoring and scheduling framework that supports resource management and trusted edge computing. We analyze the security threats in all phases of distributed edge computing, and utilize the trusted computing and public verifiability features of blockchain to ensure reliability and fairness in the trusted measurement of device computing power, the decomposition of intelligent computing tasks, the matching of task and computing power, and the verification of computing result. Finally, we implement a simulation on the edge network by performing distributed machine learning task for weather prediction, and the simulation results demonstrate the availability of our scheme.

Cross-silo federated learning (FL) is promising in facilitating data collaboration across various organizations, which greatly alleviates the information silo problem in industries and promotes the data intelligence of Internet of Things. With the advances of decentralized FL, the higher requirements of trust and privacy are put forward. Traditional FL heavily relies on a central coordinating server, which suffers from single points of failure and lacks trust in the correctness of aggregation results. What is more, the intrinsic privacy issues of FL have aroused public attention, such as gradient inversion attack in local gradients. However, the privacy of quantized gradients remains serious and lacks attention, especially the most extremely 1-bit quantization in sign-based FL. In this article, we demonstrate the potential privacy risk in sign-based FL by presenting a new gradient inversion attack, which successfully restores the original data from sign-based quantized gradients. And then we tackle the above two challenges via constructing a self-aggregation privacy-preserving FL atop blockchain, which takes advantage of a variant of ElGamal encryption to protect the privacy of local sign-based quantized gradients, and leverages the smart contract to achieve secure self-aggregation for participants without involving a centralized server. Moreover, we analyze that the proposed protocol achieves privacy and public verifiability. Finally, we evaluate the performance of the proposed protocol with a real deep learning model, and the results show that our protocol is resilient against gradient inversion attack in a decentralized environment without sacrificing learning accuracy.

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.

The emergence of electric vehicles promotes the development of Internet of Vehicle (IoV). However, there are a series of security problems to be solved in the IoV. Firstly, how to allow vehicle users to find the nearest non-queuing charging pile without detours is a challenge. Secondly, applications in the IoV are delay-sensitive, while high communication delays caused by security mechanisms would bring serious consequences to vehicles. Thirdly, the verifiability and fairness between charging and payment are difficult to be guaranteed. Aiming at the efficiency and security problems of the charging service in the IoV, this paper proposes a blockchain-based intelligent and fair IoV charging service system. According to the multi-factor constraints between vehicles and charging piles, a multi-factor IoV branch and bound algorithm is proposed to intelligently recommend charging piles for vehicles and maximize the overall energy saving. We propose the cross-area consensus protocol to achieve low latency in vehicle communication. In addition, we ensure the fairness between charging and payment through a payment channel protocol based on verifiable encrypted signatures. Finally, we implement the proposed scheme, and we put the project prototype on an open source platform is available at:https://github.com/chenruonan/blockchain-based-intelligent-and-fair-IoV-charging-service-protocol. The experimental results demonstrate the low latency and energy-saving advantages of our proposal. When the payment is executed 250 times, the delay of our proposal is only 1.54% of the normal on-chain payment time.

Mobile edge computing (MEC) is to process, analyse, store and calculate the network data at the edge of the network. When the ground infrastructure is damaged in an emergency, the unmanned aerial vehicle (UAV) formation can be rapidly deployed to undertake the task of MEC. However, there are some potential problems to be considered in UAV‐enabled MEC, such as the trust among UAVs from different sources and the stability of UAV formation network. In view of the problems existing, this study proposes a blockchain‐based architecture to build a system of mutual trust, fairness, openness, and stability in this scenario. Through the implementation of blockchain technology, key data such as device computing capacity, task allocation, and task execution process are recorded openly, transparently, and irrevocably. As multi‐party trust is built to reduce the occurrence of fraud, system participants can get a reasonable reward. On this basis, the smart contract is used to ensure that algorithms are accessible to the public, and the sub‐blockchain technology improves the stability of the system. In the case study, the simulation results show that the resource consumption and time cost of the proposed scheme is reasonable and feasible.