Liang, Hanzhong, Yue Duan, Xing Su, Xiao Li · 8 authors
As the Web3 ecosystem evolves toward a multi-chain architecture, cross-chain bridges have become critical infrastructure for enabling interoperability between diverse blockchain networks. However, while connecting isolated blockchains, the lack of cross-chain transaction pairing records introduces significant challenges for security analysis like cross-chain fund tracing, advanced vulnerability detection, and transaction graph-based analysis. To address this gap, we introduce ConneX, an automated and general-purpose system designed to accurately identify corresponding transaction pairs across both ends of cross-chain bridges. Our system leverages Large Language Models (LLMs) to efficiently prune the semantic search space by identifying semantically plausible key information candidates within complex transaction records. Further, it deploys a novel examiner module that refines these candidates by validating them against transaction values, effectively addressing semantic ambiguities and identifying the correct semantics. Extensive evaluations on a dataset of about 500,000 transactions from five major bridge platforms demonstrate that ConneX achieves an average F1 score of 0.9746, surpassing baselines by at least 20.05\%, with good efficiency that reduces the semantic search space by several orders of magnitude (1e10 to less than 100). Moreover, its successful application in tracing illicit funds (including a cross-chain transfer worth $1 million) in real-world hacking incidents underscores its practical utility for enhancing cross-chain security and transparency.
Privacy-Preserving Record Linkage (PPRL) integrates sensitive datasets from independent parties without exposing personal identifiers. Although secure multi-party computation (SMC) and homomorphic encryption ensure strong privacy, they suffer from high computational costs and poor scalability. Encoding-based methods, such as Bloom filters, are lightweight but face quality issues at scale owing to saturation and blocking inefficiencies. This study proposes a scalable, modular PPRL framework for distributed platforms. It combines Bloom filter encoding, Hamming-based locality-sensitive hashing (LSH), and Dice similarity within a MapReduce pipeline on a Hadoop distributed file system (HDFS). The system supports decentralized end-to-end linkage under semi-honest or covert adversarial models. Experiments on datasets with$100,000-500,000$records show linear scalability,$7.2 \times$speedup over cryptographic baselines, and recall degradation linked to filter saturation. A regression model captures the execution-candidate volume relationship, thereby aiding system tuning. The framework supports high-throughput, regulation-compliant linkages for healthcare, finance, and public sector use.
This paper addresses one of the most noteworthy issues in the recent virtual asset market, the privacy concerns related to token transactions of Real-World Assets tokens, known as RWA tokens. Following the advent of Bitcoin, the virtual asset market has experienced explosive growth, spawning movements to link real-world assets with virtual assets. However, due to the transparency principle of blockchain technology, the anonymity of traders cannot be guaranteed. In the existing blockchain environment, there have been instances of protecting the privacy of fungible tokens (FTs) using mixer services. Moreover, numerous studies have been conducted to secure the privacy of non-fungible tokens (NFTs). However, due to the unique characteristics of RWA tokens and the limitations of each study, it has been challenging to achieve the goal of anonymity protection effectively. This paper proposes a new token trading platform, the ARTeX, designed to resolve these issues. This platform not only addresses the shortcomings of existing methods but also ensures the anonymity of traders while enhancing safeguards against illegal activities.
Real-World Assets (RWAs) serve as a bridge between traditional financial instruments and decentralized infrastructures. By representing assets such as bonds, commodities, and real estate on blockchains, RWAs can extend the scope of decentralized finance. Industry forecasts further indicate rapid growth in tokenized RWAs after 2025, underscoring their potential role in the evolution of digital financial markets. However, in the current multi-chain environment, RWAs face challenges such as repeated authentication across multiple chains and inefficiencies arising from multi-step settlement protocols. To address these issues, we present a cross-chain framework for RWAs that emphasizes identity management, authentication, and cross-chain interaction. The framework integrates Decentralized Identifiers and Verifiable Credentials with customized attributes to support decentralized identification, and incorporates an authentication protocol based on Simplified Payment Verification to avoid redundant verification across chains. Furthermore, this paper adopts a cross-chain channel that supports efficient RWA settlements, and we refine its design so that the channel does not need to be closed immediately after each settlement, thereby reducing on-chain cost. We implement the framework and evaluate its performance via simulations, which confirm its feasibility and demonstrate improvements in efficiency for RWAs in cross-chain settings.
This chapter proposes a formal alternative to blockchain-based ledgers by reconstructing the logic of bilateral exchange relationships using projective geometry and categorical methods. We show that the normative identity of a financial contract can be faithfully embedded into a projective elliptic curve, yielding an algebraic structure isomorphic to double-entry bookkeeping. This geometric realization enables compositional transaction modeling through the elliptic group law and supports structured reasoning about contract compliance, reversibility, and balance. In contrast to distributed ledger technologies, which often fail to preserve bilateral symmetry and internal control logic, our framework enforces normative integrity by construction. We analyze the limitations of blockchain systems in supply chain transparency and auditing and present a category-theoretic model that resolves these deficiencies through local contract verification and structured composition. The resulting framework extends naturally to multi-agent reasoning, tiered supply chains, and digital audit systems, offering a mathematically rigorous foundation for trustworthy and scalable accountability infrastructures.
The demand for organ transplants is growing rapidly, yet the existing systems for organ donation face significant challenges, including lack of transparency, delays, and fraudulent activities. This paper explores a novel approach to address these issues by leveraging blockchain technology. Blockchain offers a decentralized, secure, and tamper-proof environment that can improve the efficiency and reliability of the organ donation process. By incorporating smart contracts and distributed ledger principles, the proposed system ensures that donor and recipient data are securely recorded, access is appropriately regulated, and organ matching and allocation are carried out transparently. The integration of blockchain also enhances trust and minimizes administrative overhead, making the donation process more accountable and streamlined. The study also outlines a conceptual framework for implementing this technology and highlights the potential impact on reducing illegal organ trade and ensuring ethical compliance. The study also explores how blockchain could help in maintaining a nationwide or even global donor registry that is both interoperable and scalable. In doing so, it opens avenues for real-time updates, faster allocation decisions, and the potential to curb illegal organ trafficking. Through a conceptual prototype and system design, the paper illustrates the feasibility of this approach and sets the foundation for future research and real-world implementation.
Zhi Lu, Mengyuan Zou, Samir M. Umran, Yuhao Long · 7 authors
Federated learning, as an emerging distributed machine learning approach, enables collaborative model training while protecting data privacy. However, federated learning is vulnerable to Byzantine attacks and inference attacks. Existing solutions typically require semi-honest servers to perform secure aggregation or lack effective input validation mechanisms. To address these issues, we propose RIDE, a secure aggregation protocol for decentralized federated learning with input validation. RIDE utilizes pedersen commitments and efficient zero-knowledge proofs to verify whether model updates comply with predefined constraints, ensuring client input privacy and integrity. Additionally, RIDE employs a publicly verifiable secret sharing scheme, ensuring that only validated model updates are aggregated, even in the presence of malicious clients or client dropouts. Experimental results on four real datasets demonstrate the effectiveness of our solution. For example, RIDE has a maximum bandwidth overhead of 7.11MB, which is only 1.31× that of the most popular secure aggregation protocol (CCS 2020), and the computational cost of RIDE’s execution on the CIFAR-10 L dataset is 109.88s, which is 7.28× faster than the current state-of-the-art protocol RoFL (S&P 2023).
Alejandro Guerra-Manzanares, Omar El-Herraoui, Michail Maniatakos, Farah E. Shamout
One of the key challenges of collaborative machine learning, without data sharing, is multimodal data heterogeneity in real-world settings. While Federated Learning (FL) enables model training across multiple clients, existing frameworks, such as horizontal and vertical FL, are only effective in ‘ideal’ settings that meet specific assumptions. Hence, they struggle to address scenarios where neither all modalities nor all samples are represented across the participating clients. To address this gap, we propose BlendFL, a novel FL framework that seamlessly blends the principles of horizontal and vertical FL in a synchronized and non-restrictive fashion despite the asymmetry across clients. Specifically, any client within BlendFL can benefit from either of the approaches, or both simultaneously, according to its available dataset. In addition, BlendFL features a decentralized inference mechanism, empowering clients to run collaboratively trained local models using available local data, thereby reducing latency and reliance on central servers for inference. We also introduce BlendAvg, an adaptive global model aggregation strategy that prioritizes collaborative model updates based on each client’s performance. We trained and evaluated BlendFL and other state-of-the-art baselines on three classification tasks using a large-scale real-world multimodal medical dataset and a popular multimodal benchmark. Our results highlight BlendFL’s superior performance for both multimodal and unimodal classification. Ablation studies demonstrate BlendFL’s faster convergence compared to traditional approaches, accelerating collaborative learning. Overall, in our study we highlight the potential of BlendFL for handling multimodal data heterogeneity for collaborative learning in real-world settings where data privacy is crucial, such as in healthcare and finance.
As large language models (LLMs) are used in sensitive fields, accurately verifying their computational provenance without disclosing their training datasets poses a significant challenge, particularly in regulated sectors such as healthcare, which have strict requirements for dataset use. Traditional approaches either incur substantial computational cost to fully verify the entire training process or leak unauthorized information to the verifier. Therefore, we introduce ZKPROV, a novel cryptographic framework allowing users to verify that the LLM's responses to their prompts are trained on datasets certified by the authorities that own them. Additionally, it ensures that the dataset's content is relevant to the users' queries without revealing sensitive information about the datasets or the model parameters. ZKPROV offers a unique balance between privacy and efficiency by binding training datasets, model parameters, and responses, while also attaching zero-knowledge proofs to the responses generated by the LLM to validate these claims. Our experimental results demonstrate sublinear scaling for generating and verifying these proofs, with end-to-end overhead under 3.3 seconds for models up to 8B parameters, presenting a practical solution for real-world applications. We also provide formal security guarantees, proving that our approach preserves dataset confidentiality while ensuring trustworthy dataset provenance.
The decentralized finance (DeFi) community has grown rapidly in recent years, pushed forward by cryptocurrency enthusiasts interested in the vast untapped potential of new markets. The surge in popularity of cryptocurrency has ushered in a new era of financial crime. Unfortunately, the novelty of the technology makes the task of catching and prosecuting offenders particularly challenging. Thus, it is necessary to implement automated detection tools related to policies to address the growing criminality in the cryptocurrency realm.
Jiahui Huang, Teng Huang, Changyu Dong, Sisi Duan · 5 authors
The expansion of blockchain applications, particularly on platforms like Ethereum, brings escalating security challenges as account anonymity provides breeding grounds for criminals to commit crimes and cause significant economic losses. As the mainstream architecture of de-anonymization technology, graph neural networks (GNNs) provide empirical tools for law enforcement agencies to investigate illegal activities. However, the limited expressiveness of current GNNs leads to performance degradation for Ethereum account de-anonymization. To address this challenge, we propose an innovative Local-Global Awareness (LGA) framework, which consists of a Local Structure-Aware (LSA) module and a Global Information-Aware (GIA) module. LSA integrates subgraph-level encoding strategies with local attention to enhance the capture of microscopic interactions. As a complementary measure, GIA introduces global attention to facilitate the understanding of macroscopic information. The LGA framework meticulously captures subgraph-level account behavior patterns at a granular level while simultaneously incorporating global contextual insights, demonstrating higher-level expressive power and receptive fields over conventional GNN. The efficacy of the LGA framework is corroborated by experimental evaluations conducted on the lw-AIG dataset. Our framework achieves exceptional performance, significantly outstripping state-of-the-art GNN-based methods in terms of the micro F1 score metric, with relative improvements ranging from 0.14% to 6.63%. Through its detailed and comprehensive analysis of account interactions, the LGA framework aims to provide a potent solution to the complex security challenges faced in the expanding blockchain landscape. The code for LGA is available at https://github.com/deepang-ai/LGA.
Fausto Neri da Silva Vanin, Rodrigo da Rosa Righi, Cristiano André da Costa
Blockchain technology in healthcare is gaining attention for addressing data privacy, interoperability, and health record integrity issues. Standards like HL7 FHIR and OpenEHR ensure data consistency, but privacy concerns persist under regulations like HIPAA, GDPR, and LGPD. Existing methods often store only data hashes, raising validation risks. The MEPCA model introduces a blockchain-based framework for secure health record management, focusing on on-chain EHR data processing. Key elements include Data Steward, Shared Data Vault, and Zero-Knowledge Proofs of HL7 FHIR fields. Experiments with Fully Homomorphic Encryption show enhanced security and reliability for health records, offering a robust alternative to traditional off-chain approaches.
The digital economy is one of the most dynamic, fastest growing, and impactful domains in modern economic development. To fully exploit the potential value of data as an economic resource, many governments have implemented policies to encourage and regulate data circulation and trade. Despite the increasing adoption of blockchain-based data trading platforms, a significant challenge persists: verifying data quality without compromising privacy. This deficiency reduces data utilization efficiency and limits its economic potential We propose HES, a decentralized scheme for data quality verification in transactions, which integrates homomorphic encryption, blockchain, and zero-knowledge proofs to balance privacy and verifiability. The proposed method introduces data meta-certificates to represent the original data and employs cryptographic methods to enable zero-knowledge verification. This mechanism supports the verification of both data content and quality while preserving privacy. Data buyers can verify datasets without accessing specific content or involving third-party intermediaries. In this decentralized framework, data buyers can independently verify whether a dataset meets their requirements based solely on its content and quality indicators. Our experiments show that the proposed approach solves the challenge of reaching data quality consensus in distributed scenarios, enabling data buyers and sellers to swiftly align on quality standards. This facilitates the secure circulation and trade of data assets while unlocking their economic value. The findings offer a robust, privacy-preserving consensus mechanism for evaluating data quality in transactions.
The recent EU regulation on Markets in Crypto Assets Regulation (MiCA) represents significant progress in establishing a multi-jurisdiction framework for crypto-assets that will enable the greater participation of consumers in the digital assets industry. One type of token recognized by MiCA is the asset-referenced token, where the value-bearing physical asset being referenced by the token is external to the token. We discuss several design considerations for the on-chain and off- chain metadata for the asset-referenced token that represents physical real-world assets. The EU Data Spaces provides an interesting data management framework for the off-chain metadata underpinning the MiCA asset-referenced tokens, including asset definition schemas, asset profiles, digitized asset records, and tokenized asset records. The Web3 decentralized registries for assets-related metadata should be a promising application of the data spaces framework in the EU.
R Sheeba, Jay Prakash Mahto, Syed Sabith Ansari, Zian Rajeshkumar Surani · 6 authors
The model presented in this work represents a paradigm shift that sets a completely novel standard for data distributed validation in ethical AI training. Our new paradigm integrates fault-tolerant Byzantine consensus along with zero-knowledge proofs for secured and provable auditing of data within decentralized AI systems. The framework uses a two-layer blockchain design that separates metadata anchoring from validation logs, allowing it to achieve an instantaneous compliance check time of less than one second while maintaining privacy compliance to GDPR. Key innovations comprise a sharded Merkle-Patricia Trie kind for dynamic data lineage chains, the application of differential privacy and federated learning with bias-neutralizing validation oracles, as well as the design of incentive engineering under a non-Markovian reward system for multiple agents. The results of experimentation prove that, under adversarial conditions, the detection of anomalies is 40% faster than centralized alternatives, while maintaining an integrity verification of the audit trail at 99.99%. The collaboration between AI explainability matrices and post-quantum secure voting mechanisms in this work set innovative standards for decentralized ethical oversight of mission-critical operations, thus transforming the trust dynamics among model developers, data subjects, and auditors.
Biagio Boi, Franco Cirillo, Marco De Santis, Christian Esposito
Digital health services for disease diagnosis, followup, and patient empowerment manage data that belongs to a special class of personal information, according to the General Data Protection Regulation (GDPR). For this reason, user authentication and access control are among the key security measures suggested for their protection. However, in the medical context, it is crucial to balance security and privacy support with timeliness and ease of access, which requires innovative solutions. This manuscript introduces an innovative approach leveraging Soulbound Tokens (SBTs) and Zero-Knowledge Proofs (ZKPs), particularly zk-SNARKs, to provide a privacy-aware mechanism for patient authentication in the medical domain. SBTs are utilized within an Attribute-Based Access Control (ABAC) model, ensuring that only eligible patients can access specific medical treatments. In a treatment-specific model, an SBT is issued for each diagnosis, allowing precise control but increasing management complexity. Alternatively, in a diagnosis-categorybased model, SBTs are grouped by diagnostic categories. This reduces the number of tokens and optimizes the space in the patient's wallet but sacrifices some precision in the information. Results demonstrate the timeliness of the proposed approach, with an average time of 6.82s for the release of an SBT and a maximum on-chain verification time of 15.04ms, showcasing their future adoption in a real-time environment, such as the medical context.
We present a secure and efficient string-matching platform leveraging zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) to address the challenge of detecting sensitive information leakage while preserving data privacy. Our solution enables organizations to verify whether private strings appear on public platforms without disclosing the strings themselves. To achieve computational efficiency, we integrate a sliding window technique with the Rabin-Karp algorithm and Rabin Fingerprint, enabling hash-based rolling comparisons to detect string matches. This approach significantly reduces time complexity compared to traditional character-by-character comparisons. We implement the proposed system using gnark, a high-performance zk-SNARK library, which generates succinct and verifiable proofs for privacy-preserving string matching. Experimental results demonstrate that our solution achieves strong privacy guarantees while maintaining computational efficiency and scalability. This work highlights the practical applications of zero-knowledge proofs in secure data verification and contributes a scalable method for privacy-preserving string matching.
The unprecedented growth of digital health ecosystems, fueled by electronic health records (EHRs), wearable devices, telemedicine, and AI-driven diagnostics, has amplified the critical need for reliable data provenance mechanisms. Provenance, defined as the comprehensive history of data generation, access, transformation, and transfer, ensures that stakeholders—including patients, clinicians, insurers, researchers, and regulators—can trust the authenticity, integrity, and accountability of healthcare information. Traditional provenance systems, often centralized, are vulnerable to insider manipulation, cyberattacks, data silos, and audit inefficiencies, thereby undermining trust and regulatory compliance. Distributed Ledger Systems (DLS), encompassing blockchain, permissioned ledgers, and Directed Acyclic Graphs (DAGs), offer a paradigm shift by enabling immutable, transparent, and tamper-evident provenance trails across diverse healthcare stakeholders. This manuscript provides an in-depth exploration of DLS-enabled healthcare data provenance by reviewing current literature, identifying research gaps, and developing a methodological framework tested through simulated experiments. Empirical evaluation demonstrates that distributed ledgers reduce provenance validation time by 57–71%, accelerate audit processes by up to 70%, and significantly enhance regulatory traceability under HIPAA and GDPR requirements. Moreover, patient-centric smart contracts and decentralized identifiers foster individual ownership and interoperability, reshaping data governance models toward inclusivity and transparency. While challenges such as scalability, energy efficiency, and privacy-preserving erasure remain, the findings highlight DLS as a transformative infrastructure for establishing trustworthy healthcare ecosystems. The study concludes by recommending hybrid ledger architectures, cryptographic privacy enhancements, and supportive policy frameworks to ensure sustainable, ethical, and globally interoperable healthcare data provenance systems.
Aishwarya Parab, P. Pradhan, Yogesh Simmhan, Arnab K. Paul
The increasing availability of data from diverse sources, including trusted entities such as governments, as well as untrusted crowd-sourced contributors, demands a secure and trustworthy environment for storage and retrieval. Blockchain, as a distributed and immutable ledger, offers a promising solution to address these challenges. This short paper studies the feasibility of a blockchain-based framework for secure data storage and retrieval across trusted and untrusted sources, focusing on provenance, storage mechanisms, and smart contract security. Through initial experiments using Hyper Ledger Fabric (HLF), we evaluate the storage efficiency, scalability, and feasibility of the proposed approach. This study serves as a motivation for future research to develop a comprehensive blockchain-based storage and retrieval framework.
Ahmed Mohamed Abdallah, Heba K. Aslan, Mohamed S. Abdallah, Young Im Cho · 5 authors
In recent years, the rapid growth of cryptocurrency markets has highlighted the urgent need for advanced security solutions capable of addressing a spectrum of unique threats, from phishing and wallet hacks to complex blockchain vulnerabilities. This paper presents a comprehensive approach to fortifying cryptocurrency systems by harnessing the structural symmetry inherent in transactional patterns. By leveraging local large language models (LLMs), embeddings, and vector databases, we develop an intelligent and scalable security expert system that exploits symmetry-based anomaly detection to enhance threat identification. Cryptocurrency networks face increasing threats from sophisticated attacks that often exploit asymmetric vulnerabilities. To counteract these risks, we propose a novel security expert system that integrates symmetry-aware analysis through LLMs and advanced embedding techniques. Our system efficiently captures symmetrical transaction patterns, enabling robust detection of anomalies and threats while preserving structural integrity. By integrating a modular framework with LangChain and a vector database (Chroma DB), we achieve improved accuracy, recall, and precision by leveraging the symmetry of transaction distributions and behavioral patterns. This work sets a new benchmark for LLM-driven cybersecurity solutions, offering a scalable and adaptive approach to reinforcing the security symmetry in cryptocurrency systems. The proposed expert system was evaluated using a benchmark dataset of cryptocurrency transactions, including real-world threat scenarios involving phishing, fraudulent transactions, and blockchain anomalies. The system achieved an accuracy of 92%, a precision of 89%, and a recall of 93%, demonstrating a 10% improvement over existing security frameworks. Compared to traditional rule-based and machine learning-based detection methods, our approach significantly enhances real-time threat detection while reducing false positives. The integration of LLMs with embeddings and vector retrieval enables more efficient contextual anomaly detection, setting a new benchmark for AI-driven security solutions in the cryptocurrency domain.
Luiz Koodi Hotta, Carlos Trucíos, Pedro L. Valls Pereira, Mauricio Zevallos
Recent studies have suggested that more complex models than GARCH are better suited for forecasting cryptocurrency risk measures, such as Value-at-Risk and Expected Shortfall. Among these studies, some highlight the advantages of MSGARCH models over traditional GARCH models. While improvements over single-regime GARCH models have been observed by using MSGARCH, the literature has only focused on the MSGARCH specification proposed by Haas, Mittnik and Paolella (Journal of Financial Econometrics, 2004) overlooking several other well-established MSGARCH specification alternatives. In this paper, we illustrate that exploring alternative MSGARCH specifications can lead to improvements in risk measure performance, emphasizing the potential benefits of using several specifications.
Jens Ernstberger, Jan Lauinger, Yulin Wu, Arthur Gervais · 5 authors
Transport Layer Security (TLS) is foundational for safeguarding client-server communication. However, it does not extend integrity guarantees to third-party verification of data authenticity. If a client wants to present data obtained from a server, it cannot convince any other party that the data has not been tampered with. TLS oracles ensure data authenticity beyond the client-server TLS connection, such that clients can obtain data from a server and ensure provenance to any third party, without server-side modifications. Generally, a TLS oracle involves a third party, the verifier, in a TLS session to verify that the data obtained by the client is accurate. Existing protocols for TLS oracles are communication-heavy, as they rely on interactive protocols. We present ORIGO, a TLS oracle with constant communication. Similar to prior work, ORIGO introduces a third party in a TLS session, and provides a protocol to ensure the authenticity of data transmitted in a TLS session, without forfeiting its confidentiality. Compared to prior work, we rely on intricate details specific to TLS 1.3, which allow us to prove correct key derivation, authentication and encryption within a Zero Knowledge Proof (ZKP). This, combined with optimizations for TLS 1.3, leads to an efficient protocol with constant communication in the online phase. Our work reduces online communication by 375× and online runtime by up to 4.6×, compared to prior work.