Blockchain Papers

Zhenyu Xu, Junliang Du

No abstract is available for this record.

Krutant Dongare

The Verification and Validation of Certificate Using Blockchain system is designed to provide a secure, transparent, and tamper-proof mechanism for issuing and verifying educational and professional certificates. Traditional verification methods are often prone to forgery, delays, and administrative inefficiencies due to centralized databases and manual validation. This system leverages blockchain technology to store certificate data in an immutable distributed ledger, ensuring authenticity and preventing manipulation. Additionally, the integration of the InterPlanetary File System (IPFS) provides decentralized, lowcost storage for certificates, while an Android-based interface simplifies issuance and verification processes. By enabling decentralized trust, rapid verification, and cross-border accessibility, this system enhances transparency, reduces fraudulent activities, and establishes a reliable digital framework for secure credential management

Jennifer Bala, Sikiru O. SUBAIRU, Noel M. DOGONYARO, Joseph A. OJENIYI · 5 authors

Blockchain technology, particularly Ethereum, has revolutionized decentralized finance by enabling transparent, secure, and programmable smart contracts. However, these same features have created avenues for financial crimes such as Ponzi schemes, where fraudulent actors exploit pseudonymity and the absence of centralized oversight to deceive investors. This study develops an optimized hybrid detection model that combines eXtreme Gradient Boosting (XGBoost) and Gated Recurrent Units (GRU) to identify Ponzi schemes in Ethereum transaction networks. The model integrates XGBoost’s capability for structured feature learning with GRU’s temporal sequence modeling to capture both static and dynamic behavioral patterns of smart contracts. Using a dataset of 3,866 labeled Ethereum contracts obtained from Kaggle, the research employed advanced preprocessing, temporal sequence enrichment, and class balancing through SMOTE-TS to mitigate data imbalance. Bidirectional optimization, incorporating attention-enhanced GRUs and Bayesian hyperparameter tuning for XGBoost, further improved learning performance and generalization. The model was evaluated using precision, recall, F1-score, ROC-AUC, and PR-AUC, achieving higher detection accuracy of 99% (F1-score = 0.945, ROC-AUC = 0.983) than standalone XGBoost or GRU models. Results demonstrate the hybrid model’s superior ability to detect temporal and statistical anomalies, reducing false negatives and improving early detection of fraudulent contracts. The approach contributes a scalable and interpretable framework for real-time Ponzi detection in blockchain ecosystems. This research not only enhances the reliability of Ethereum’s financial ecosystem but also offers regulators and developers a novel tool for proactive fraud prevention. Future work could extend this framework to multi-chain detection systems and real-time forensic monitoring.

Jie Chen, Liangmin Wang

Fuzzing is a widely used technique for detecting vulnerabilities in smart contracts, which generates transaction sequences to explore the execution paths of smart contracts. However, existing fuzzers are falling short in detecting sophisticated vulnerabilities that require specific attack transaction sequences with proper inputs to trigger, as they (i) prioritize code coverage over vulnerability discovery, wasting considerable effort on non-vulnerable code regions, and (ii) lack semantic understanding of stateful contracts, generating numerous invalid transaction sequences that cannot pass runtime execution. In this paper, we propose SmartFuzz, a novel collaborative reflective fuzzer for smart contract vulnerability detection. It employs large language model-driven agents as the fuzzing engine and continuously improves itself by learning and reflecting through interactions with the environment. Specifically, we first propose a new Continuous Reflection Process (CRP) for fuzzing smart contracts, which reforms the transaction sequence generation as a self-evolving process through continuous reflection on feedback from the runtime environment. Then, we present the Reactive Collaborative Chain (RCC) to orchestrate the fuzzing process into multiple sub-tasks based on the dependencies of transaction sequences. Furthermore, we design a multi-agent collaborative team, where each expert agent is guided by the RCC to jointly generate and refine transaction sequences from both global and local perspectives. We conduct extensive experiments to evaluate SmartFuzz's performance on real-world contracts and DApp projects. The results demonstrate that SmartFuzz outperforms existing state-of-the-art tools: (i) it detects 5.8\%-74.7\% more vulnerabilities within 30 minutes, and (ii) it reduces false negatives by up to 80\%.

R. K. Shyamasundar

No abstract is available for this record.

Chiara Braghin, Elvinia Riccobene, Simone Valentini

No abstract is available for this record.

Mustafa Doger, Sennur Ulukus

Bitcoin's (BTC) Difficulty Adjustment Algorithm (DAA) has been a source of vulnerability for incentive attacks such as selfish mining, block withholding and coin hopping strategies. In this paper, first, we rigorously study the short-term revenue change per hashpower of the adversarial and honest miners for these incentive attacks. To study the long-term effects, we introduce a new efficiency metric defined as the revenue/cost per hashpower per time for the attacker and the honest miners. Our results indicate that the short-term benefits of intermittent mining strategies are negligible compared to the original selfish mining attack, and in the long-term, selfish mining provides better efficiency. We further demonstrate that a coin hopping strategy between BTC and Bitcoin Cash (BCH) relying on BTC DAA benefits the loyal honest miners of BTC in the same way and to the same extent per unit of computational power as it does the hopper in the short-term. For the long-term, we establish a new boundary between the selfish mining and coin hopping attack, identifying the optimal efficient strategy for each parameter. For block withholding strategies, it turns out, the honest miners outside the pool profit from the attack, usually even more than the attacker both in the short-term and the long-term. Moreover, a Power Adjusting Withholding (PAW) attacker does not necessarily observe a profit lag in the short-term. In other words, even without a difficulty adjustment, a PAW attacker makes profits. It has been long thought that the profit lag of selfish mining is among the main reasons why such an attack has not been observed in practice. We show that such a barrier does not apply to PAW and relatively small pools are at an immediate threat.

Syed Muhammad Nadeem Kadery, Rafat Arrahman Al Haque, Md. Mamun Habib

The global apparel industry is highly competitive, demanding innovation and cost-effective production models. Bangladesh, a leading textile exporter, struggles with long lead times (90–100 days) compared to rivals like China and Pakistan. Relying only on cheap labor makes it difficult to sustain market leadership, especially as Cambodia and Vietnam rise. The COVID-19 crisis accelerated blockchain adoption worldwide, particularly in apparel, where it enhances transparency, authentication, and efficiency, strengthening brand image and profits. Blockchain, introduced by Satoshi Nakamoto, offers secure, decentralized data sharing, preventing breaches and fostering trust. For Bangladesh, integrating blockchain into the RMG supply chain could reduce costs, improve stakeholder collaboration, and ensure competitiveness. Research highlights that traditional supply chains depend heavily on labor and contractors, but blockchain’s distributed ledger can streamline information flow among stakeholders. To maintain dominance in the digitized 21st century, Bangladesh must embrace blockchain, ensuring sustainability, customer satisfaction, and global relevance.

Xiaomei Yan, Yong Li, Yan Zhu

Trading of data is increasingly prevalent as data gain significant economic value, but existing data exchange schemes often suffer from third-party dependency, high verification costs, or inadequate protection of fairness and confidentiality. An efficient decentralized fair exchange scheme for data trading which uses cryptographic commitment scheme and smart contract was proposed in this paper. Our solution guarantees exchange fairness, which requires payments and data to be exchanged correctly between the data buyer and the data seller. First, we design a data verification method with constant verification cost by using polynomial commitments, ensuring that the buyer receives the data matching an agreed-upon commitment. Second, we employ smart contracts to complete the atomic exchange of data and funds, and design a key transmission method by using the properties of bilinear pairings to ensure the confidentiality of trading data. Moreover, our scheme was proved to satisfy the desired security properties: seller fairness, buyer fairness and confidentiality. Simulation results demonstrate the efficiency and practicality of the proposed scheme.

Qiufan Wu, Han Wang, Hui Li, Kaili Shao · 7 authors

The classical CAP theorem reveals fundamental limitations in distributed system design, namely the impossibility of simultaneously achieving strong Consistency, high Availability, and Partition tolerance. As an important distributed ledger technology, blockchain systems also face these constraints. Recent research has attempted to alleviate this problem through consensus layer or physical layer optimization techniques. However, these methods fail to achieve optimal availability due to the mismatch problem between network layer and physical layer topologies. Therefore, this paper designs a P2P broadcasting protocol that matches physical topology structures at the network layer, namely Matching-Gossip, serving as an intelligent adapter between physical topology and consensus protocols to collectively address the CAP trilemma. Experimental results demonstrate that blockchain systems based on Matching-Gossip achieve trilemma efficiency coefficients exceeding 95%, simultaneously meeting engineering requirements for strong consistency, high availability, and partition tolerance, thereby breaking through traditional CAP limitations on blockchain system design.

Vening, Edwin Jean-Paul

10.5281/zenodo.17605813 chaos structure complexity sequences / test files / public domain Chaos Complexity Domain Sequencing"Maximum Entropy Equilibrium"sha384sum OUTFN_BASE-OUTFN_VER-OUTFN_VERMIN-20221230191340.OUTFN_EXT.1069cbf8cebedf73040848960d915d728f8ebce64de339e57c03984b9b125065571ee73cba2fbe8324d57770631f22d3c27download Value Char Occurrences Fraction 0 4000000106 0.500000 1 3999999894 0.500000Total: 8000000000 1.000000Entropy = 1.000000 bits per bit.Optimum compression would reduce the sizeof this 8000000000 bit file by 0 percent.Chi square distribution for 8000000000 samples is 0.00, and randomlywould exceed this value 99.81 percent of the times.Arithmetic mean value of data bits is 0.5000 (0.5 = random).Monte Carlo value for Pi is 3.141394237 (error 0.01 percent).Serial correlation coefficient is 0.000013 (totally uncorrelated = 0.0).sha384sum OUTFN_BASE-OUTFN_VER-OUTFN_VERMIN-20230103155948.OUTFN_EXT.107d6275873f72a0edc7585db17bba50cdfb4a5097f3f50ac7b69eaa85ae8ec95975fb187579a5b05ff0c69ca71378fe71d download Value Char Occurrences Fraction 0 4000000107 0.500000 1 3999999893 0.500000Total: 8000000000 1.000000Entropy = 1.000000 bits per bit.Optimum compression would reduce the sizeof this 8000000000 bit file by 0 percent.Chi square distribution for 8000000000 samples is 0.00, and randomlywould exceed this value 99.81 percent of the times.Arithmetic mean value of data bits is 0.5000 (0.5 = random).Monte Carlo value for Pi is 3.141257485 (error 0.01 percent).Serial correlation coefficient is 0.000004 (totally uncorrelated = 0.0). DATA MORGANA COMMUNICATIONS AUTHOR/ EDWIN J. VENINGEDITOR EDWIN J. VENINGCORRESPONDENCE ADMIN@DATAMORGANA.NETWEBSITE SPAWN HTTPS://WWW.DATAMORGANA.NETRELEASED DD 20230525 [ YYYYMMDD ]EDIT REV.DD 20231208 over 20230726REF <symbolic base> See addendum:- binary ambiguity is expectedIntroduction in Dutch : page 2 20230726 crt0 Addendum: The test vectors presented here stem from the design of a custom generator, originally intended to outperform competitors in various categories of "randomness" generation. The goal was to achieve chaotic streams that exceeded the capabilities of other contenders, without relying on traditional methods for balancing distribution qualities. The resulting system incorporates parametric high-gain, maximum entropy equilibrium functions and methods, with output files available for download from this page. These files are derived from this work and should be used with caution. Historical Context: In 2019, a proposal was made to enhance the cryptographic subsystem of operating systems through a novel approach. This concept involved hardening the system with a new cryptographic processing "idea" of operation(s), integrated within a fresh confidence model. This idea was presented as the open-source project: /dev/entropy, a Unix non-blocking character device designed for non-disclosed ZKP (Zero-Knowledge Proof) seasonal or projected transactions/operations. The goal was to bootstrap system entropy pools using unique host identification, confidence constraints, and host signature processing in its own ZKP design (a system verifier capsule). /dev/entropy was intended to serve as the system entropy pool, which would be well-documented and securely stored. The author and programmer asserted that chaining cryptographic functions could weaken their security, leading to a proposal for entropy pools that would re-seed cryptographic functions in the host stack using non-linear, complexity-driven methods. These operations were intentionally designed to be opaque to prevent exposure, aiming to mitigate known mechanical noise attack vectors and thwart binary dissection. The processing would involve a novel use of "RAM" or "held latent memory." The project concluded in 2019 but remains a significant influence on the development of unique event processing and symbolic information transformations. As for the test vectors, no claims are made regarding their randomness or indexing properties. Envisioned Applications for the Methods and Functions: High-speed calibration of scientific instruments High-gain precision, offering persistent increases in resolution for guidance systems, telemetry, and high-availability scheduling (real-time systems) Persistence of identification tokens, tokenizing information by range, sequence hinting (*), as suggested in the ZKP paper ZKP 'circuitry' / 'gadgets' with enhanced properties, allowing for directional confidence balancing and omni-directional jumps, encoding with unique event processing such as spacetime locality encoding Real-time processing improvements, introducing new priority-type scheduling and domain sequencing (correlated context, with no known limits or recursion results) Application of "lossy" parity and "hashing" in new contexts, utilizing range hinting or the development of a symbolic encoded sequence that persists in noisy systems. The ratio is under testing. Expected hardware development: Domain sequencing through event processors with hardened/optical circuitry and one-way functions These methods aim to serve as a critical infrastructure carrier post-quantum Cryptography (PQC), offering potential solutions for complex network topologies and signal semantics for future interstellar applications. This approach leverages spatial and referential qualities without sudden collapse, adding the Temporal Domain Cryptography from 2015 as part of the ongoing evolution. DISCLAIMER: The contents of these vectors may contain the densest information to date, with an inherent carbon footprint that requires careful handling. Due to the dense nature of this data, it may cause local mechanical friction and, in extreme cases, could lead to combustion. As with any significant discovery, proceed with caution. Note: This is not the recommended practice in the narrowing binary domain of information. For reference: CACert Random Number Results — "No Entropy Here" home https://www.datamorgana.net

Salunkhe Rajan Yashwant

Incident reporting systems are integral to maintaining accountability and transparency across critical domains such as cybersecurity, healthcare, and public governance. However, existing centralized mechanisms are prone to manipulation, data loss, and unauthorized modifications. This paper proposes 'IntegriChain', an intelligent and decentralized incident reporting framework that combines Blockchain technology and Artificial Intelligence (AI). The system ensures tamper-proof data storage through SHA-256 hashing and distributed ledger technology while leveraging AI for incident classification, anomaly detection, and risk prediction. This hybrid approach improves security, reliability, and efficiency in reporting workflows. The framework is designed to serve as a scalable solution applicable to multi-domain reporting systems where trust, immutability, and intelligent analysis are critical.

Bing Zhang, Guijuan Wang, Zhongyuan Yu, Anming Dong · 5 authors

Cross-chain payment, serving as critical infrastructure for multi-chain ecosystem interoperability, confronts the fundamental challenge of simultaneously ensuring privacy preservation, regulatory compliance, and quantum-resistant security—objectives that are inherently difficult to reconcile. This paper proposes a Lattice-based Dynamic Privacy-preserving Cross-chain Payment Scheme (LDPCPS) that innovatively integrates advanced cryptographic primitives. Specifically, LDPCPS employs a privacy-preserving scalar product (PPSP) protocol enabling ciphertext-domain aggregation and verification, constructs a dynamic regulatory framework using signatures of knowledge (SoK) for zero-knowledge compliance proofs and risk-triggered traceability, and implements proxy re-encryption to facilitate seamless quantum-resistant key migration. Experimental results demonstrate that LDPCPS has significant superiority over state-of-the-art alternatives in quantum resistance, computational efficiency, and regulatory adaptability, thereby establishing a robust foundation for secure and compliant cross-chain transactions.

Linlin Chen

The increasing complexity and widespread deployment of smart contracts (SCs) on blockchain platforms have heightened the need for interpretable and verifiable representations. While smart contracts encode critical business logic, their low-level implementations remain difficult for auditors and regulators to interpret. To bridge this semantic gap, we propose a structure-aware instruction-tuning framework that translates Solidity functions into Business Process Model and Notation (BPMN) diagrams using large language models (LLMs). Our approach constructs a high-quality dataset of 15K Solidity-BPMN pairs through embedding-based clustering, prompt engineering, and multi-template augmentation. We fine-tune DeepSeek-Coder using LoRA for efficient domain adaptation, enabling the model to generate syntactically valid and semantically faithful BPMN structures. Experimental results show that our fine-tuned model outperforms GPT-4o, Gemini, and baseline LLMs in both structural precision and semantic fidelity. This work lays the groundwork for structure-level explainability of smart contracts and supports future research in code-to-process modeling and blockchain compliance analysis.

Krithika Rao, Shakil Khan, Bruce Singh, Nagulapati Kiran · 5 authors

Regulatory sandboxes—controlled environments where firms test innovations under regulatory supervision—have been adopted globally to manage fintech and crypto experimentation. This paper compares sandbox approaches and policy effectiveness for decentralized finance (DeFi) across the European Union, the United States, and the Asia-Pacific. Using a mixed-methods design (document analysis, stakeholder reports, and an illustrative quantitative model), we assess objectives, design choices, risk controls, and outcomes (market access, investor protection, and innovation diffusion). Findings show the EU’s pan-European coordination aims to harmonize testing and legal clarity; the US displays fragmented, agency-led pilot initiatives with stronger enforcement posture; Asia-Pacific exhibits rapid, varied adoption with jurisdictional leaders (Singapore, Hong Kong, Australia) using sandboxes as precursors to more formal rulebooks. Policy effectiveness depends on clarity of legal scope, cross-agency coordination, and well-designed exit and scaling rules. We conclude with policy recommendations and a research agenda for empirically measuring sandbox effectiveness for DeFi.

Amol Murgai, M. Vijay Bhasker Reddy, M. P. Vani, Prof. Supriya Jagtap · 6 authors

Cloud edge convergence is enabling real time personalized learning but come with security and trust issues. Centralized models cause single points of failure, and lack transparency. This paper proposes a framework for decentralized access control and auditability of blockchains and tamper resistance to try to make smart contracts work well. Prototype using Ethereum and IPFS: low latency in authorization, better discretion is achieved comparing with the centralized approaches. This paper presents a blockchain technology framework that will facilitate secure data sharing process in cloud-edge learning networks. The framework has smart contracts that enforce the dynamic policy of access to provide auditable records of all data transactions. A prototype implementation that is tested in a simulated federated learning scenario shows how the system can deal with access decisions with low latency and data integrity and audit beyond. These findings indicate that blockchain can be one of the possible ways of moving to decentralized, policybased information cooperation in the education sector, where information protection and institutional trust are critical.

Authors unavailable

Food security is a serious global issue, concerning the availability, accessibility, safety, and stability of food. The agri-food supply chain, which connects farms to consumers, often faces problems such as fragmented data systems, poor transparency, and low trust between stakeholders. These problems make decision-making slow and reduce the quality and safety of food. During the Fourth Industrial Revolution (IR4.0), digital technologies are transforming many industries, including food sector. Among them, blockchain has emerged as a critical enabler for traceability and accountability. However, balancing transparency and privacy remains a major challenge as sensitive business data must be protected. Without solving this issue, many stakeholders are not ready to accept blockchain solutions. This study analyses current blockchain limitations and proposes a privacy-preserving blockchain solution for agri-food supply chains. Using the Design Science Research Methodology (DSRM), this work identifies key privacy gaps, designs a solution integrating selective data sharing, access control, and privacy-preserving techniques such as zero-knowledge proofs and differential privacy and outlines future empirical validation through prototype implementation. These features aim to balance open traceability with the need to keep important information private. The results suggest that a privacy-preserving blockchain can enhance trust, protect private data, and maintain transparency in the food chain. This makes the system more resilient and reliable. At the same time, it supports the United Nations goals, especially Goal 2 (Zero Hunger) and Goal 12 (Responsible Consumption and Production), by helping to build food supply chains that are safe, fair, and sustainable.

Mauro Clavijo-Herrera, Rolando Trujillo-Rasua, Carles Anglés Tafalla

The integrity and traceability of digital photographic evidence represent a critical factor during forensic investigations, especially when this evidence undergoes technical transformations, such as cropping or resolution enhancement. Ensuring that these modifications remain transparent, verifiable, and attributable is essential to maintaining the value of the evidence during an investigation. To meet these requirements, existing systems typically rely on blockchain-based implementations within permissioned networks or provide only limited support for image transformations. As a result, they often lack the flexibility and transparency required for open or decentralized forensic scenarios. In this paper, we propose an endorsement-based image forensics system that leverages public blockchain to record the lifecycle and verify the authenticity of images. Our system employs hybrid encryption to provide confidentiality of uploaded images while simultaneously ensuring that they remain auditable and non-repudiable. The system supports different trust models and enables users to assess the trustworthiness of an image’s provenance data directly and indirectly. Direct trust is achieved by validating an image transformation through reproducible functions or zero-knowledge proofs; indirect trust is enabled through publicly recorded endorsements. Our design achieves low gas costs and provides confidentiality, verifiability, and traceability guarantees, improving upon previous approaches without relying on permissioned infrastructures.

Roshani S. Nage, Sanjay Dorle

Secure authentication along with malware detection are very important steps in modern cloud or IoT environment, with, privacy, accountability, and resilience against advanced threats. The present day anonymous authentication protocols reportedly have a high cryptographic overhead, low traceability, or static privacy mechanisms, while the current IoT malware forensic approaches happen to suffer from gradient leakage, low adaptability to zero day attacks, and slow resilience. This paper presents a comprehensive multi model framework combining five novel methods. The Dual Ledger Accountability Embedded Authentication (DLAA) model combines a primary blockchain with a secondary lightweight audit ledger and zero knowledge proofs, enabling revocable accountability without identity disclosure. The Layered Privacy Gradient Synthesis (LPGS) network applies adaptive differential privacy through learned gradient perturbations, balancing anonymity with service utility. The Quantum Inspired Entropy Guided Authentication Matrix (QEAM) replaces the key exchange with entropy driven, quantum inspired encoding, enabling faster keyless authentication. For IoT forensics, the Federated Swarm Vector Autoencoder Forensics (FSVAF) framework uses swarm optimized federated learning to detect anomalies in compressed latent space, reducing gradient leakage and improving zero day detection possibilities. The Temporal Hybrid Graph Reasoning Engine (THGRE) fuses symbolic rules with neural inference over evolving knowledge graphs for quick malware traceback. The experimental output reveals that the authentication time is reduced by 38%, with 94% malware detection accuracy in adaptive attack conditions, and is able to resolve forensics up to 67% more rapidly than previous static approaches with significantly reduced overhead. This framework collectively enhance privacy, accountability, scalability, and forensic dependability, making it efficient solution for next generation cloud and IoT ecosystems.

Arthur Carvalho, Ewerton Vinícius Pereira da Silva, Gustavo Carvalho Hamade

This scientific article analyzes Law No. 14,478/2022, the “Legal Framework for Cryptocurrencies” in Brazil. Adopting a legal-dogmatic approach, the study maps the regulatory advances, such as the creation of an initial normative framework, the criminalization of certain conducts, and the formalization of consumer protection. Conversely, it explores the law’s limits and gaps, emphasizing the omission of asset segregation and the challenges posed by the decentralized nature of Decentralized Finance (DeFi) and tax uncertainties. A comparative analysis with the European Union’s MiCA Regulation contextualizes Brazil’s choice for a principles-based model. The study concludes that the law’s effectiveness will depend on infra-legal regulation and the legal system’s ability to adapt to the market’s dynamism.

Xinyu Ren, Xuanrui Xiong, Dan Hu, Sensen Qiu · 6 authors

The proliferation of resource-constrained Internet of Things (IoT) devices poses formidable security challenges, rendering traditional centralized authentication mechanisms impractical. To address this issue, this paper proposes AMAKA (Anonymous Mutual Authentication and Key Agreement), a novel blockchain-fortified protocol specifically designed for IoT environments. AMAKA utilizes smart contracts to establish a robust framework for device lifecycle management, including registration, updates, and revocation, enabling fine-grained access control under the authority of the equipment manufacturer. The protocol's core synergizes Schnorr signatures with noninteractive zero-knowledge proofs to deliver strong guarantees of mutual authentication, user anonymity, unlinkability, perfect forward secrecy, and conditional traceability. We formally verify AMAKA's security against a wide range of attacks by employing the ProVerif tool under an active adversary model. Furthermore, a prototype deployed on a private Ethereum network demonstrates its practical viability, confirming low on-chain overhead, minimal storage demands, and high computational efficiency. Therefore, AMAKA provides a balanced, secure, and scalable authentication solution for large-scale IoT ecosystems.

Wang Yi, Zhou Guangtao, Chen Hong

Blockchain, as a new distributed technology, plays important roles in various areas. But due to unsupervised transaction model of blockchain, Decentralized Application (DAPP) mainly focus on limited Decentralized Finance (DEFI) areas such as lending or trading, leaving enterprise and government application untouched. This paper propose a supervised transaction algorithm with identity privacy enabled on blockchain to tackle this problem. In essence, we encrypt transaction before its submission onto the chain, and execute transaction after supervisor's approval. The paper satisfies the compliance of regulation in finance and social application, and ensures that identity privacy of trader can be protected. In this way, blockchain can be adapted to broader domains without technology compromise.

Xiangfu Liu, Teng Huang, Caiyan Tan, Jiahui Huang · 6 authors

Vulnerability attacks targeting smart contracts have caused significant losses of digital assets. Many approaches based on static analysis, fuzzing, and deep learning have been proposed for detecting contract vulnerabilities. However, most existing methods only support vulnerability detection within individual contracts. When contracts interact with each other through external calls, these methods fail to perform effective cross-contract security analysis, leading to false negatives and false positives. To address these limitations, we propose CrossMiner, a deep learning-based approach for vulnerability detection in contract interaction scenarios. CrossMiner enables comprehensive risk assessment for cross-contract security through trace analysis of function call chains. Specifically, CrossMiner first constructs a cross-contract dependency graph based on function call chains to effectively model inter-contract dependencies and network dynamics, and collect semantic information about contract interactions. Then, it employs a heterogeneous graph neural network with a two-level attention mechanism to finely extract and integrate complex features from the dependency graph, ultimately achieving precise risk assessment and vulnerability detection. We evaluate the effectiveness of CrossMiner on three types of smart contract vulnerabilities: reentrancy, timestamp dependency, and transaction state dependency. Experimental results demonstrate that CrossMiner achieves the best performance among all baseline methods, improving detection accuracy by 5.52%, 4.94%, and 5.60% for these vulnerabilities, and the F1 scores are improved by 5.44%, 5.02%, and 5.40%, respectively.

Wenjun Zhu, Jianan Hong, Yingjie Xue, Jiayue Zhou · 5 authors

As the blockchain ecosystem continues to diversify, the lack of interoperability among heterogeneous blockchain systems has become a critical bottleneck, leading to fragmented data silos and limited collaboration. Although numerous cross-chain protocols—such as atomic swaps, sidechains, and relay-based mechanisms—have been introduced to address this issue, they often face significant challenges related to privacy, security, and decentralization. In this paper, we propose a novel cross-chain protocol that enhances traditional hash-locking mechanisms by integrating zero-knowledge proofs and chameleon hash functions. Our approach ensures strong path confidentiality, such that reconstructing the payment path is computationally infeasible under the discrete logarithm assumption, even in partially compromised networks. Additionally, we introduce a multi-path atomic swap framework that supports concurrent routing and preserves transactional autonomy, enabling users to flexibly select preferred payment paths. We evaluate the performance through theoretical analysis and simulation. Comparative results demonstrate that our solution achieves secure atomicity with minimal trust assumptions and improved latency compared to existing methods.