Current Ethereum fraud detection methods rely on context-independent, numerical transaction sequences, failing to capture semantic of account transactions. Furthermore, the pervasive homogeneity in Ethereum transaction records renders it challenging to learn discriminative account embeddings. Moreover, current self-supervised graph learning methods primarily learn node representations through graph reconstruction, resulting in suboptimal performance for node-level tasks like fraud account detection, while these methods also encounter scalability challenges. To tackle these challenges, we propose LMAE4Eth, a multi-view learning framework that fuses transaction semantics, masked graph embedding, and expert knowledge. We first propose a transaction-token contrastive language model (TxCLM) that transforms context-independent numerical transaction records into logically cohesive linguistic representations. To clearly characterize the semantic differences between accounts, we also use a token-aware contrastive learning pre-training objective together with the masked transaction model pre-training objective, learns high-expressive account representations. We then propose a masked account graph autoencoder (MAGAE) using generative self-supervised learning, which achieves superior node-level account detection by focusing on reconstructing account node features. To enable MAGAE to scale for large-scale training, we propose to integrate layer-neighbor sampling into the graph, which reduces the number of sampled vertices by several times without compromising training quality. Finally, using a cross-attention fusion network, we unify the embeddings of TxCLM and MAGAE to leverage the benefits of both. We evaluate our method against 21 baseline approaches on three datasets. Experimental results show that our method outperforms the best baseline by over 10% in F1-score on two of the datasets.
Yifan Jia, Ye Tian, Liguo Zhang, Yanbin Wang · 6 authors
Ethereum's rapid ecosystem expansion and transaction anonymity have triggered a surge in malicious activity. Detection mechanisms currently bifurcate into three technical strands: expert-defined features, graph embeddings, and sequential transaction patterns, collectively spanning the complete feature sets of Ethereum's native data layer. Yet the absence of cross-paradigm integration mechanisms forces practitioners to choose between sacrificing sequential context awareness, structured fund-flow patterns, or human-curated feature insights in their solutions. To bridge this gap, we propose KGBERT4Eth, a feature-complete pre-training encoder that synergistically combines two key components: (1) a Transaction Semantic Extractor, where we train an enhanced Transaction Language Model (TLM) to learn contextual semantic representations from conceptualized transaction records, and (2) a Transaction Knowledge Graph (TKG) that incorporates expert-curated domain knowledge into graph node embeddings to capture fund flow patterns and human-curated feature insights. We jointly optimize pre-training objectives for both components to fuse these complementary features, generating feature-complete embeddings. To emphasize rare anomalous transactions, we design a biased masking prediction task for TLM to focus on statistical outliers, while the Transaction TKG employs link prediction to learn latent transaction relationships and aggregate knowledge. Furthermore, we propose a mask-invariant attention coordination module to ensure stable dynamic information exchange between TLM and TKG during pre-training. KGBERT4Eth significantly outperforms state-of-the-art baselines in both phishing account detection and de-anonymization tasks, achieving absolute F1-score improvements of 8-16% on three phishing detection benchmarks and 6-26% on four de-anonymization datasets.
Ethereum smart contracts hold tens of billions of USD in DeFi and NFTs, yet comprehensive security analysis remains difficult due to unverified code, proxy-based architectures, and the reliance on manual inspection of complex execution traces. Existing approaches fall into two main categories: anomaly transaction detection, which flags suspicious transactions but offers limited insight into specific attack strategies hidden in execution traces inside transactions, and code vulnerability detection, which cannot analyze unverified contracts and struggles to show how identified flaws are exploited in real incidents. As a result, analysts must still manually align transaction traces with contract code to reconstruct attack scenarios and conduct forensics. To address this gap, TraceLLM is proposed as a framework that leverages LLMs to integrate execution trace-level detection with decompiled contract code. We introduce a new anomaly execution path identification algorithm and an LLM-refined decompile tool to identify vulnerable functions and provide explicit attack paths to LLM. TraceLLM establishes the first benchmark for joint trace and contract code-driven security analysis. For comparison, proxy baselines are created by jointly transmitting the results of three representative code analysis along with raw traces to LLM. TraceLLM identifies attacker and victim addresses with 85.19\% precision and produces automated reports with 70.37\% factual precision across 27 cases with ground truth expert reports, achieving 25.93\% higher accuracy than the best baseline. Moreover, across 148 real-world Ethereum incidents, TraceLLM automatically generates reports with 66.22\% expert-verified accuracy, demonstrating strong generalizability.
This paper considers five extensions for Chromium-based browsers in order to determine how effective can browser-based defenses against cryptojacking available to regular users be. We've examined most popular extensions - MinerBlock, AdGuard AdBlocker, Easy Redirect && Prevent Cryptojacking, CoinEater and Miners Shield, which claim to be designed specifically to identify and stop illegal cryptocurrency mining. An empirically confirmed dataset of 373 distinct cryptojacking-infected websites which was assembled during multi-stage procedure, was used to test those extensions. The results showed that all plugins in question had significant performance limits. Easy Redirect and Miners Shield only blocked 6 and 5 websites respectively, while MinerBlock had the greatest detection rate at only 27% (101/373 sites blocked). Most concerningly, despite promises of cryptojacking prevention, AdGuard (which has over 13 million users) and CoinEater were unable to identify any of the compromised websites. These results demonstrate serious flaws in cryptojacking detection products targeted for regular users, since even the best-performing specimen failed to detect 73% of attacks. The obvious difference between advertised capabilities and real performance highlights the urgent need for either accessibility improvements for laboratory-grade detection technologies that show 90%+ efficiency in controlled environment or fundamental upgrades to current commonly used extensions.
This paper presents a novel multi-layered hybrid security approach aimed at enhancing lightweight encryption for IoT-Cloud systems. The primary goal is to overcome limitations inherent in conventional solutions such as TPA, Blockchain, ECDSA and ZSS which often fall short in terms of data protection, computational efficiency and scalability. Our proposed method strategically refines and integrates these technologies to address their shortcomings while maximizing their individual strengths. By doing so we create a more reliable and high-performance framework for secure data exchange across heterogeneous environments. The model leverages the combined potential of emerging technologies, particularly Blockchain, IoT and Cloud computing which when effectively coordinated offer significant advancements in security architecture. The proposed framework consists of three core layers: (1) the H.E.EZ Layer which integrates improved versions of Hyperledger Fabric, Enc-Block and a hybrid ECDSA-ZSS scheme to improve encryption speed, scalability and reduce computational cost; (2) the Credential Management Layer independently verifying data integrity and authenticity; and (3) the Time and Auditing Layer designed to reduce traffic overhead and optimize performance across dynamic workloads. Evaluation results highlight that the proposed solution not only strengthens security but also significantly improves execution time, communication efficiency and system responsiveness, offering a robust path forward for next-generation IoT-Cloud infrastructures.
Internet of Things (IoT) devices constantly generate heterogeneous data streams, driving demand for continuous, decentralized intelligence. Federated Lifelong Learning (FLL) provides an ideal solution by incorporating federated learning and lifelong learning. However, the extended lifecycle of FLL in IoT systems increases their vulnerability to persistent attacks. This problem is exacerbated by the single point of failure. Furthermore, the single point of trust created by the central server hinders reliable auditing for long-term threats. Blockchain technology provides a tamper-proof foundation for trustworthy FLL. Nevertheless, directly applying blockchain to FLL significantly increases computational and retrieval costs with the expansion of the knowledge base, slowing down the training on resource-constrained IoT devices. To address these challenges, we propose LiFeChain, a lightweight blockchain for secure and efficient federated lifelong learning with minimal on-chain disclosure and bidirectional verification. LiFeChain is the first blockchain tailored for FLL. It incorporates two complementary mechanisms: the Proof-of-Model-Correlation (PoMC) consensus on the server, which couples learning and unlearning mechanisms to mitigate negative transfer; and Segmented Zero-knowledge Arbitration (Seg-ZA) at the client, which detects and arbitrates abnormal committee behavior without compromising privacy. LiFeChain is a plug-and-play component that can be seamlessly integrated into existing FLL algorithms for IoT applications. To demonstrate its practicality and performance, we implement LiFeChain in representative FLL algorithms with Hyperledger Fabric under 6 attacks. Theoretical analysis and extensive evaluations demonstrate that LiFeChain effectively mitigates long-term attacks, and significantly reduces latency and storage overhead compared to state-of-the-art blockchain solutions.
The increasing adoption of smart home devices and IoT-based security systems presents significant opportunities to enhance convenience, safety, and risk management for homeowners and service providers. However, secure onboarding-provisioning credentials and establishing trust with cloud platforms-remains a considerable challenge. Traditional onboarding methods often rely on centralized Public Key Infrastructure (PKI) models and manufacturer-controlled keys, which introduce security risks and limit the user's digital sovereignty. These limitations hinder the widespread deployment of scalable IoT solutions. This paper presents a novel onboarding framework that builds upon existing network-layer onboarding techniques and extends them to the application layer to address these challenges. By integrating consortium blockchain technology, we propose a decentralized onboarding mechanism that enhances transparency, security, and monitoring for smart home architectures. The architecture supports device registration, key revocation, access control management, and risk detection through event-driven alerts across dedicated blockchain channels and smart contracts. To evaluate the framework, we formally model the protocol using the Tamarin Prover under the Dolev-Yao adversary model. The analysis focuses on authentication, token integrity, key confidentiality, and resilience over public channels. A prototype implementation demonstrates the system's viability in smart home settings, with verification completing in 0.34 seconds, highlighting its scalability and suitability for constrained devices and diverse stakeholders. Additionally, performance evaluation shows that the blockchain-based approach effectively handles varying workloads, maintains high throughput and low latency, and supports near real-time IoT data processing.
Remote Procedure Call (RPC) services have become a primary gateway for users to access public blockchains. While they offer significant convenience, RPC services also introduce critical privacy challenges that remain insufficiently examined. Existing deanonymization attacks either do not apply to blockchain RPC users or incur costs like transaction fees assuming an active network eavesdropper. In this paper, we propose a novel deanonymization attack that can link an IP address of a RPC user to this user's blockchain pseudonym. Our analysis reveals a temporal correlation between the timestamps of transaction confirmations recorded on the public ledger and those of TCP packets sent by the victim when querying transaction status. We assume a strong passive adversary with access to network infrastructure, capable of monitoring traffic at network border routers or Internet exchange points. By monitoring network traffic and analyzing public ledgers, the attacker can link the IP address of the TCP packet to the pseudonym of the transaction initiator by exploiting the temporal correlation. This deanonymization attack incurs zero transaction fee. We mathematically model and analyze the attack method, perform large-scale measurements of blockchain ledgers, and conduct real-world attacks to validate the attack. Our attack achieves a high success rate of over 95% against normal RPC users on various blockchain networks, including Ethereum, Bitcoin and Solana.
Fine-tuning large language models (LLMs) is crucial for adapting them to specific tasks, yet it remains computationally demanding and raises concerns about correctness and privacy, particularly in untrusted environments. Although parameter-efficient methods like Low-Rank Adaptation (LoRA) significantly reduce resource requirements, ensuring the security and verifiability of fine-tuning under zero-knowledge constraints remains an unresolved challenge. To address this, we introduce VeriLoRA, the first framework to integrate LoRA fine-tuning with zero-knowledge proofs (ZKPs), achieving provable security and correctness. VeriLoRA employs advanced cryptographic techniques -- such as lookup arguments, sumcheck protocols, and polynomial commitments -- to verify both arithmetic and non-arithmetic operations in Transformer-based architectures. The framework provides end-to-end verifiability for forward propagation, backward propagation, and parameter updates during LoRA fine-tuning, while safeguarding the privacy of model parameters and training data. Leveraging GPU-based implementations, VeriLoRA demonstrates practicality and efficiency through experimental validation on open-source LLMs like LLaMA, scaling up to 13 billion parameters. By combining parameter-efficient fine-tuning with ZKPs, VeriLoRA bridges a critical gap, enabling secure and trustworthy deployment of LLMs in sensitive or untrusted environments.
Determining the optimal block size is crucial for achieving high throughput in blockchain systems. Many studies have focused on tuning various components, such as databases, network bandwidth, and consensus mechanisms. However, the impact of block size on system performance remains a topic of debate, often resulting in divergent views and even leading to new forks in blockchain networks. This research proposes a mathematical model to maximize performance by determining the ideal block size for Hyperledger Fabric, a prominent consortium blockchain. By leveraging machine learning and solving the model with a genetic algorithm, the proposed approach assesses how factors such as block size, transaction size, and network capacity influence the block processing time. The integration of an optimization solver enables precise adjustments to block size configuration before deployment, ensuring improved performance from the outset. This systematic approach aims to balance block processing efficiency, network latency, and system throughput, offering a robust solution to improve blockchain performance across diverse business contexts.
Cross-chain bridges play a vital role in enabling blockchain interoperability. However, due to the inherent design flaws and the enormous value they hold, they have become prime targets for hacker attacks. Existing detection methods show progress yet remain limited, as they mainly address single-chain behaviors and fail to capture cross-chain semantics. To address this gap, we leverage heterogeneous graph attention networks, which are well-suited for modeling multi-typed entities and relations, to capture the complex execution semantics of cross-chain behaviors. We propose BridgeShield, a detection framework that jointly models the source chain, off-chain coordination, and destination chain within a unified heterogeneous graph representation. BridgeShield incorporates intra-meta-path attention to learn fine-grained dependencies within cross-chain paths and inter-meta-path attention to highlight discriminative cross-chain patterns, thereby enabling precise identification of attack behaviors. Extensive experiments on 51 real-world cross-chain attack events demonstrate that BridgeShield achieves an average F1-score of 92.58%, representing a 24.39% improvement over state-of-the-art baselines. These results validate the effectiveness of BridgeShield as a practical solution for securing cross-chain bridges and enhancing the resilience of multi-chain ecosystems.
We explore the feasibility of deploying Bitcoin as the shared monetary standard between Earth and Mars, accounting for physical constraints of interplanetary communication. We introduce a novel primitive, Proof-of-Transit Timestamping (PoTT), to provide cryptographic, tamper-evident audit trails for Bitcoin data across high-latency, intermittently-connected links. Leveraging Delay/Disruption-Tolerant Networking (DTN) and optical low-Earth-orbit (LEO) mesh constellations, we propose an architecture for header-first replication, long-horizon Lightning channels with planetary watchtowers, and secure settlement through federated sidechains or blind-merge-mined (BMM) commit chains. We formalize PoTT, analyze its security model, and show how it measurably improves reliability and accountability without altering Bitcoin consensus or its monetary base. Near-term deployments favor strong federations for local settlement; longer-term, blind-merge-mined commit chains (if adopted) provide an alternative. The Earth L1 monetary base remains unchanged, while Mars can operate a pegged commit chain or strong federation with 1:1 pegged assets for local block production. For transparency, if both time-beacon regimes are simultaneously compromised, PoTT-M2 (and PoTT generally) reduces to administrative assertions rather than cryptographic time-anchoring.
Youwei Huang, Jianwen Li, Bin Hu, Sen Fang · 6 authors
Malicious developer intents in smart contracts constitute significant security threats to decentralized applications, leading to substantial economic losses. Prior work introduced SmartIntentNN, a deep learning model for detecting unsafe developer intents. By combining the Universal Sentence Encoder, a K-means clustering-based intent highlighting mechanism, and a Bidirectional Long Short-Term Memory (BiLSTM) network, the model achieved an F1 score of 0.8633 on an evaluation set of 10,000 real-world smart contracts across ten distinct intent categories. This paper presents SmartIntentV2 (Smart Contract Intent Neural Network Version 2). The primary enhancement is the integration of a BERT-based pre-trained programming language model, which we domain-adaptively pre-train on a dataset of 16,000 real-world smart contracts using a Masked Language Modeling objective. SmartIntentV2 retains the BiLSTM-based multi-label classification network for intent detection. On the same evaluation set of 10,000 smart contracts, it achieves superior performance with an accuracy of 0.9789, precision of 0.9090, recall of 0.9476, and an F1 score of 0.9279, substantially outperforming its predecessor and other baseline models. Notably, SmartIntentV2 also delivers a 65.5% relative improvement in F1 score over GPT-4.1 on this specialized task. These results establish SmartIntentV2 as a new state-of-the-art model for smart contract intent detection.
The Internet of Things (IoT) is applied in various fields, and the number of physical devices connected to the IoT is increasingly growing. There are significant challenges to the IoT's growth and development, mainly due to the centralized nature and large-scale IoT networks. The emphasis on the decentralization of IoT's architecture can overcome challenges to IoT's capabilities. A promising decentralized platform for IoT is blockchain. Owing to IoT devices' limited resources, traditional consensus algorithms such as PoW and PoS in the blockchain are computationally expensive. Therefore, the PoA consensus algorithm is proposed in the blockchain consensus network for IoT. The PoA selects the validator as Turn-based selection (TBS) that needs optimization and faces system reliability, energy consumption, latency, and low scalability. We propose an efficient, lightweight blockchain for decentralizing IoT architecture by using virtualization and clustering to increase productivity and scalability to address these issues. We also introduce a novel PoA based on the Weight-Based-Selection (WBS) method for validators to validate transactions and add them to the blockchain. By simulation, we evaluated the performance of our proposed WBS method as opposed to TBS. The results show reduced energy consumption, and response time, and increased throughput.
With the rapid growth of Electric Vehicle (EV) technology, EVs are destined to shape the future of transportation. The large number of EVs facilitates the development of the emerging vehicle-to-grid (V2G) technology, which realizes bidirectional energy exchanges between EVs and the power grid. This has led to the setting up of electricity markets that are usually confined to a small geographical location, often with a small number of participants. Usually, these markets are manipulated by intermediaries responsible for collecting bids from prosumers, determining the market-clearing price, incorporating grid constraints, and accounting for network losses. While centralized models can be highly efficient, they grant excessive power to the intermediary by allowing them to gain exclusive access to prosumers \textquotesingle price preferences. This opens the door to potential market manipulation and raises significant privacy concerns for users, such as the location of energy providers. This lack of protection exposes users to potential risks, as untrustworthy servers and malicious adversaries can exploit this information to infer trading activities and real identities. This work proposes a secure, decentralized exchange market built on blockchain technology, utilizing a privacy-preserving Automated Market Maker (AMM) model to offer open and fair, and equal access to traders, and mitigates the most common trading-manipulation attacks. Additionally, it incorporates a scalable architecture based on geographical dynamic sharding, allowing for efficient resource allocation and improved performance as the market grows.
The development of blockchain technology has significantly enhanced the security and transparency of personal information and transaction records. Concurrent with the advancement of blockchain technology and the emergence of the digital currency ecosystem, the internet has evolved from a paradigm dominated by information flow to one driven by value flow. Consequently, the concept of token has gained widespread dissemination, and the electronic token under investigation in this thesis is a development of this concept. The application of electronic tokens has become pervasive with the development of the internet, but the functionality of these tokens is often limited, and issues related to trust remain significant challenges. This study proposes innovative solutions to address the deficiencies in traditional electronic token systems, including the issuance of tokens, the heterogeneity of issuance standards, and the lack of democracy. The solutions are based on distributed storage, anti-tampering mechanisms, consensus protocols, and transparent, traceable storage. Additionally, the study employs NFTs, smart contracts, and RSA blind signatures, among other key technologies, to construct a system based on blockchain technology. The process integrates the decentralised management and centralised operation models, aligning them with the national policy directives. The developed solution enables the full utilisation of blockchain technology's advantages while also fostering community participation. Consequently, it establishes a secure, legal, reliable, and dynamic electronic certification system.
As blockchain technology continues to evolve, the security of smart contracts has increasingly drawn attention from both academia and industry. The Move language, with its unique resource model and linear type system, provides a solid foundation for the security of digital assets. However, smart contracts still face new security challenges due to developer programming errors and the potential risks associated with cross-module interactions. This paper systematically analyzes the limitations of existing security tools within the Move ecosystem and reveals their unique vulnerability patterns. To address these issues, it introduces MoveScanner, a static analysis tool based on a control flow graph and data flow analysis architecture. By incorporating cross-module call graph tracking, MoveScanner can effectively identify five key types of security vulnerabilities, including resource leaks, weak permission management, and arithmetic overflows. In terms of design, MoveScanner adheres to a modular principle, supports bytecode-level analysis and multi-chain adaptation, and introduces innovative resource trajectory tracking algorithms and capability matrix analysis methods, thereby significantly reducing the false positive rate. Empirical results show that MoveScanner achieved 88.2% detection accuracy in benchmark testing, filling the gap in security tools in the Move ecosystem. Furthermore, this paper identifies twelve new types of security risks based on the resource-oriented programming paradigm and provides a theoretical foundation and practical experience for the development of smart contract security mechanisms. Future work will focus on combining formal verification and dynamic analysis techniques to build a security protection framework covering the entire contract lifecycle
Yagmur Yigit, Mehmet Ali Erturk, Kerem Gursu, Berk Canberk
Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and verifiable physical-digital binding, making them unsuitable for latency-sensitive and distributed deployments. To address this gap, we introduce PRZK-Bind, a lightweight and decentralised authentication protocol that combines Schnorr-based zero-knowledge proofs with elliptic curve cryptography to establish secure, real-time correspondence between physical entities and DTs without relying on pre-shared secrets. Simulation results show that PRZK-Bind significantly improves performance, offering up to 4.5 times lower latency and 4 times reduced energy consumption compared to cryptography-heavy baselines, while maintaining false acceptance rates more than 10 times lower. These findings highlight its suitability for future smart city deployments requiring efficient, resilient, and trustworthy DT authentication.
With the advent of quantum computing, cryptocurrencies that rely on blockchain technology face mounting cryptographic vulnerabilities. This paper presents a comprehensive literature review evaluating how quantum algorithms, specifically Shors and Grovers, could disrupt the foundational security mechanisms of cryptocurrencies. Shors algorithm poses a threat to public-key cryptographic schemes by enabling efficient factorization and discrete logarithm solving, thereby endangering digital signature systems. Grovers algorithm undermines hash-based functions, increasing the feasibility of fifty one percent attacks and hash collisions. By examining the internal mechanisms of major cryptocurrencies such as Bitcoin, Ethereum, Litecoin, Monero, and Zcash, this review identifies specific vulnerabilities in transaction and consensus processes. It further analyses the current hardware limitations of quantum systems and estimates when such attacks could become feasible. In anticipation, it investigates countermeasures including Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and protocol-level modifications such as memory-intensive proof-of-work algorithms and multi-signature schemes. The discussion integrates recent advancements in quantum error correction, hardware scalability, and NIST-standardized cryptographic algorithms. This review concludes that while quantum computers are not yet advanced enough to pose an immediate threat, proactive integration of quantum-resistant solutions is essential. The findings underscore the urgent need for cryptocurrencies to adopt post-quantum cryptographic standards to preserve the decentralized trust, integrity, and security that define blockchain-based digital cryptocurrencies.
Yang Li, Hanjie Wang, Yuanzheng Li, Jiazheng Li · 5 authors
Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites. While federated learning enables privacy-preserving collaboration without sharing raw data, it remains vulnerable to anomalous updates and privacy leakage during parameter exchange. These challenges are amplified in open industrial environments, necessitating zero-trust mechanisms where no participant is inherently trusted. To address these challenges, this work proposes ZTFed-MAS2S, a zero-trust federated learning framework that integrates a multi-head attention-based sequence-to-sequence imputation model. ZTFed integrates verifiable differential privacy with non-interactive zero-knowledge proofs and a confidentiality and integrity verification mechanism to ensure verifiable privacy preservation and secure model parameters transmission. A dynamic trust-aware aggregation mechanism is employed, where trust is propagated over similarity graphs to enhance robustness, and communication overhead is reduced via sparsity- and quantization-based compression. MAS2S captures long-term dependencies in wind power data for accurate imputation. Extensive experiments on real-world wind farm datasets validate the superiority of ZTFed-MAS2S in both federated learning performance and missing data imputation, demonstrating its effectiveness as a secure and efficient solution for practical applications in the energy sector.
Blockchain is a Distributed Ledger Technology (DLT) that offers numerous benefits including decentralization, transparency, efficiency, and reduced costs. Hence, blockchain has been included in many fields. Blockchain relies on cryptographic protocols (especially public-key cryptography and hash functions) to achieve many essential sub-routines. However, the increased progress of quantum computation and algorithms has threatened the security of many traditional cryptosystems. Therefore, this represents a serious risk for the existing blockchain technology. For example, SHA-256 and the Elliptic Curve Digital Signature Algorithm (ECDSA) cryptosystems can be compromised by Shor s and Grover s quantum algorithms in the foreseeable future. Post-Quantum Cryptography (PQC) is a basic solution for resisting these quantum attacks. Applying PQC to blockchains results in creating Post-Quantum Blockchains (PQB). Thus, this paper aims to review the threats imposed by quantum computers on classical blockchain technology and provide useful guidelines on PQB security to blockchain researchers. The paper focuses on the challenges and opportunities of future work direction in this field.
Zero-Knowledge Proofs (ZKPs) have emerged as a powerful tool for secure and privacy-preserving computation. ZKPs enable one party to convince another of a statement's validity without revealing anything else. This capability has profound implications in many domains, including machine learning, blockchain, image authentication, and electronic voting. Despite their potential, ZKPs have seen limited deployment because of their exceptionally high computational overhead, which manifests primarily during proof generation. To mitigate these overheads, a (growing) body of researchers has proposed hardware accelerators and GPU implementations of both kernels and complete protocols. Prior art spans a wide variety of ZKP schemes that vary significantly in computational overhead, proof size, verifier cost, protocol setup, and trust. The latest and widely used ZKP protocols are intentionally designed to balance these trade-offs. One particular challenge in modern ZKP systems is supporting complex, high-degree gates using the SumCheck protocol. We address this challenge with a novel programmable accelerator to efficiently handle arbitrary custom gates via SumCheck. Our accelerator achieves upwards of $1000\times$ geomean speedup over CPU-based SumChecks across a range of gate types. We include this unit in zkPHIRE, a programmable, full-system accelerator that accelerates the HyperPlonk protocol. zkPHIRE achieves $1486\times$ geomean speedup over CPU and $11.87\times$ geomean speedup over the state-of-the-art at iso-area. Together, these results demonstrate compelling performance while scaling to large problem sizes (upwards of $2^{30}$ constraints) and maintaining small proof sizes ($4-5$ KB).
This paper studies proof-of-work Nakamoto consensus protocols under bounded network delays, settling two long-standing questions in blockchain security: What is the most effective attack on block safety under a given block confirmation latency? And what is the resulting probability of safety violation? A Markov decision process (MDP) framework is introduced to precisely characterize the system state (including the blocktree and timings of all blocks mined), the adversary's potential actions, and the state transitions due to the adversarial action and the random block arrival processes. An optimal attack, called bait-and-switch, is proposed and proved to maximize the adversary's chance of violating block safety by "beating Nakamoto in the race". The exact probability of this violation is calculated for any given confirmation depth using Markov chain analysis, offering fresh insights into the interplay of network delay, confirmation rules, and blockchain security.
The very high growth of Intelligent Transportation Systems (ITS) has generated an urgent requirement for secure, effective, and context-aware data sharing mechanisms, especially over heterogeneous and geographically dispersed settings. This work suggests a new architecture that combines a relay chain-driven encryption system with a modified Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme to tackle the double impediment of dynamic access and low-latency communication. The model proposes a context-aware smart contract on a worldwide relay chain that checks against data properties, including event type, time, and geographical region, to specify the suitable level of encryption policy. From such relay-directed judgment, On-Board Units (OBUs) encrypt data end-to-end by utilising CP-ABE and store ciphertext inside localised regional blockchains, preventing dependence on symmetric encryption or off-chain storage. High-sensitivity events are secured with firm, multi-attribute access rules, whereas common updates use light policies to help reduce processing burdens. The crypto system also adds traceability and low-latency revocation, with global enforcement managed through the relay chain. This distributed, scalable model provides a proper balance between responsiveness in real time and security and is extremely apt for next-gen vehicular networks that function across multi-jurisdictional domains.