NOXFORD ID is a seven-layer privacy-preserving identity verification architecture designed to address a specific fraud vulnerability in cross-institutional identity validation: the brittleness of exact-match verification against ordinary, legitimate variation in how names and dates are recorded across institutions. The architecture combines hardened Bloom-filter cryptographic linkage (privacy-preserving record linkage), field-separated fuzzy matching, a probabilistic decision engine with an explicit human-review tier, a tamper-evident cross-institutional integrity ledger, a genuine Schnorr zero-knowledge proof of identity knowledge, replay-resistant institution-specific key derivation, and a Root Certificate Authority layer aligned with Nigeria's NIMC Act 2026, which designates the National Identity Management Commission as Root Certification Authority for the country's National Public Key Infrastructure. Each layer is independently implemented and empirically tested against a labeled test corpus, with results reported transparently, including design flaws discovered and corrected during development. The paper documents a measured reduction in false rejection of legitimate citizens from 57% to 14% relative to a representative exact-match baseline, while maintaining zero fraud slip-through in testing, and explicitly states the system's current limitations, including evaluation corpus scale, dependence on enrollment-data integrity, and pending network-level threat-detection integration.
This deliverable (D8.8) presents the contributions of the PLIADES project to advancing the European Interoperability Framework (EIF) and interoperability standardization, with a focus on building a modular, scalable, and trustworthy data sharing ecosystem. The work conducted in Task 8.6 evaluates the project’s alignment with the EIF’s four layers-legal, organizational, semantic, and technical—and extends this analysis through ISO/IEC 19941’s five interoperability facets—policy, behavior, semantic, syntactic, and transport. By applying a general interoperability-framework approach, the deliverable assesses the interoperability maturity of PLIADES across six use cases in domains including mobility, energy, healthcare, green deal/ circular economy, energy, and industry. These use cases demonstrate how PLIADES supports dynamic, cross-domain data integration through advanced AI capabilities such as federated learning, explainable AI, and declarative querying—while ensuring legal compliance, data sovereignty, and semantic clarity. The project engages directly with EU standardization and governance initiatives—including SEMIC, DSSC, and the European Trusted Data Framework standardisation request to ensure alignment with emerging regulations like the Data Act. PLIADES actively contributes to the EU’s semantic and technical interoperability agenda through workshops, conference participation (e.g., SEMIC 2025, ENDORSE 2025), and alignment with the IDS Rulebook and the Dataspace Protocol. Gaps identified in current interoperability models—such as limited runtime interoperability, lack of support for decentralized AI, and insufficient metadata expressiveness—are addressed through actionable recommendations. PLIADES proposes enhancements to semantic alignment, dynamic querying, and data governance architectures, helping to shape the next iteration of European data policy frameworks. Ultimately, this report underscores PLIADES’ strategic role in fostering cross-border, cross-sector data interoperability. By operationalizing both EIF and ISO-based principles through real-world use cases and aligning with EU standardization initiatives, PLIADES delivers a blueprint for trusted, AI-enabled, sovereign data spaces that drive innovation and support Europe’s digital transition. PLIADES stands for an advanced AI AI-enabled framework for Full Data Lifecycles Optimisation and Data Spaces Integration. Our mission is to revolutionize how data is utilised across various sectors, from mobility to healthcare, manufacturing to energy, and beyond. PLIADES envisions a future where diverse sectors are seamlessly interconnected, enhancing efficiency and interoperability. We aim to provide cutting cutting-edge data and services that drive advancements in Cooperative, Connected, and Automated Mobility (CCAM), Advanced Driver Assistance & Autonomous Driving (ADAS/AD), and HumanHuman-Robot Interaction (HRI).
Data valuation is a foundational task in data marketplaces, where a Shapley-value attribution determines how a buyer's payment is distributed among data providers. Typically, the marketplace operator runs this attribution alone, requiring participants and external auditors to trust scores they cannot independently recompute on the underlying private data. While zero-knowledge proofs (ZKPs) can theoretically reconcile this conflict between privacy and verifiability, existing ZK valuation systems fail to scale to real-world marketplace demands due to prohibitive proving times or the requirement to disclose validation cohorts. We present ZK-Value, a practical, end-to-end ZK data-valuation system. Our solution bridges the scalability gap through a fully co-designed architecture: (1) LSH-Shapley, a locality-based valuation primitive that replaces expensive pairwise distance metrics with per-bucket collision counts; (2) ZK-LSH-Shapley, a tailored ZKP protocol that drastically reduces witness size by encoding these counts into bucket-level histograms rather than naive per-pair tensors; and (3) structural proof-system optimizations, specifically super-oracle batching and sparsity skipping. Evaluated across 12 standard datasets, ZK-Value delivers valuation quality on par with state-of-the-art baselines (within 0.033 AUROC of exact KNN-Shapley), while generating proofs in seconds to minutes and outperforming specialized ZK baselines by 12.6x to 68.1x in proving time, with verification in under 4.6 s.
Every day, innumerable items are lost and unclaimed in shopping malls, restaurants, airports, and other public places. While some lost and found systems exist, they are often non-automated, poorly structured, and vulnerable to data loss. We present a blockchain- and AI-based platform that integrates Internet of Things (IoT) for real-time tracking and zero-knowledge proofs (ZKPs) for privacy-preserving verification. In this platform, users can report lost or found items, for which information hashes are generated and then stored on the blockchain to ensure immutability, transparency, and trust. Artificial intelligence is used to compare lost items with potential found items to reduce the complexity of searching. To evaluate the AI component, we used a transfer learning technique with pre-trained CNN models, namely ResNet50, VGG16, and MobileNetV3, on the Caltech-256 dataset filtered to 10 relevant classes (1,219 images), attaining 95.46% ±1.09% accuracy in 5-fold cross-validation for ResNet50 without augmentation, 93.99% ±3.44% on holdout test, and 94.54% ±3.29% under Gaussian blur for robustness. Feature embeddings yielded top-1 matching accuracy of 89.01% and top-5 of 95.60%, outperforming recent image-matching baselines in noisy real-world conditions while maintaining sub-0.0003 s inference time. These results establish a scalable, trustworthy global ecosystem for lost-and-found management
The transition from isolated distributed ledgers to a unified “Internet of Value” is hindered by the lack of efficient, verifiable, and privacy-preserving cross-chain data retrieval mechanisms. While asset bridging has matured, generalized data indexing remains a critical bottleneck, constrained by the semantic gap between heterogeneous storage layouts and the prohibitive verification tax of cryptographic proofs. In this paper, we present HyperCross, a novel semantic-aware zero-knowledge indexing framework designed to bridge this divide. We first formalize the heterogeneous cross-chain storage optimization problem (HCCSOP) and prove its NP-completeness. To tackle this, HyperCross employs a synergistic tri-layered architecture. At the semantic layer, we introduce a unified data abstraction (UDA) that leverages category-theoretic functors and schema morphisms to ensure mathematically rigorous state mapping for both simple assets and complex smart contract logic. At the indexing layer, a zero-knowledge learning index (ZKLI) shifts prediction intelligence to the client side, integrating zk-SNARKs with silent oblivious transfer to achieve constant-time verification (O(1)) while concealing access patterns. Finally, a multi-level cache (MLC) utilizes predictive prefetching with Δ-bounded staleness to mask network latency. Extensive evaluations demonstrate that HyperCross reduces query latency by 2.4× and storage overhead by 40% compared to state-of-the-art baselines, establishing a scalable foundation for data-intensive inter-chain applications.
The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of tools, yet existing solutions buckle under \( \mathcal{O}(N) \) complexity and centralized governance. Instead of building another fragile overlay, we propose ToolDNS, a radical framework that retrofits semantic tool discovery onto the Internet's most resilient substrate: the Domain Name System (DNS). By embedding functional intent and organizational trust into a hierarchical namespace, ToolDNS transforms an expensive semantic search into a series of lightweight, \( \mathcal{O}(\log N) \) name resolutions. We introduce three protocol-compliant enhancements to enable decentralized governance and semantic pruning: partially unfolded names, EDNS0 intent payloads, and logical subdomains. To rigorously evaluate this approach across the fragmented tooling landscape, we construct and release a large-scale heterogeneous benchmark comprising \( 33,688 \) real-world tools spanning MCP, A2A, RESTful, and Skill protocols. On this dataset, ToolDNS slashes the per-query search space by \( 95.26\% \) while matching state-of-the-art retrieval accuracy. Furthermore, its UDP-native design reduces discovery latency by orders of magnitude compared to HTTP-based registries. Our work demonstrates that scalable AI interoperability requires not more middleware, but a smarter utilization of the infrastructure already beneath our feet.
Distributed recruitment is changing the way companies hire people and is also creating new problems for Human Resources teams. It is now much easier for people to fake documents, pretend to be someone else, or carry out employment fraud, while old methods like manual checks, emails, and database queries cannot keep up with tricks such as fake videos or forged papers. SafeHire is designed to solve these problems as a system that checks if people are who they claim to be and fits modern hiring needs. Instead of slow and easily fooled methods, it uses Zero-Knowledge Proofs with the Anon-Aadhaar protocol so people can prove their identity without sharing private information. Government IDs are verified offline using XML signature validation, and academic records are stored securely using SHA-256 hashing so they cannot be changed. To check documents, SafeHire uses Jaro-Winkler and Levenshtein distance methods to find small errors and also verifies employers using Corporate Identification Numbers (CIN). All data is protected so only the right people can access it through strict access rules. SafeHire is a faster and more secure way to hire, using system-based verification connected to trusted records to reduce the weaknesses of older applicant tracking systems and make hiring more reliable.
Traditional Byzantine Fault Tolerance (BFT) consensus algorithms effectively tolerate node behavioral faults but lack the ability to verify the quality of input data. This makes them vulnerable to security risks from low-quality or “compliant yet malicious” data in data-driven applications. To address this gap, we propose a Data-Quality-Driven Byzantine Fault Tolerance algorithm based on Zero-Knowledge Proofs, called Q-BFT. The algorithm introduces a “quality gate” prior to classic BFT consensus—an on-chain verification phase that uses zk-SNARKs and is automated by smart contracts. This allows nodes to prove in zero-knowledge that their data meets predefined thresholds for accuracy, completeness, and consistency without exposing raw data. Passing the verification becomes a prerequisite for joining consensus voting. We design a two-layer smart contract architecture that efficiently orchestrates off-chain proof generation and on-chain automated verification. Experiments show that in a 100-node network with 30% malicious nodes, Q-BFT improves the consensus success rate from 41.5% (with PBFT) to 96.4%, while maintaining federated learning global model accuracy above 88%, in contrast to the model collapse (< 20% accuracy) observed under a traditional BFT protocol. The system achieves an average verification latency below 0.65 s and a throughput of 735 TPS(Transactions Per Second), striking an effective balance among security, privacy preservation, and operational efficiency. By enforcing privacy-preserving data quality verification as a mandatory gate before consensus, Q-BFT thus provides a high-assurance foundation for data-sensitive and privacy-critical applications. It addresses the core vulnerability of traditional consensus in scenarios like federated learning, where model integrity depends on participant data quality, and trustworthy data markets, where transaction validity requires assured data authenticity without exposing the data itself.
We present NanoZK, a zero-knowledge proof system for verifiable LLM inference: clients and third-party auditors check that a provider executed the advertised model on a committed input without learning weights or activations. NanoZK introduces a layerwise proof framework that decomposes transformer inference into independently provable layers linked by a SHA-256 commitment chain, yielding constant-size sub-circuit proofs (3.5-3.7 KB; about 83 KB total at L=12), comparable in total size to and substantially more parallelizable than prior ZKML's monolithic 101-126 KB proofs. We prove compositional soundness and zero-knowledge under standard assumptions, design 16-bit lookup-table approximations for softmax, GELU, and normalization with measured perplexity degradation below 1e-4 across six model/dataset combinations, and add a Fisher-information-guided audit-budget triage as an efficiency tool (full soundness still requires verifying every layer). On CPU the MLP sub-circuit proves in about 6.3 s prove-only (about 43 s setup plus prove) with about 22 ms verification at any width; attention prove-only time scales from 0.9 s (d=16) to 184 s (d=256); full-block end-to-end proofs are measured to d=128, with a projected GPU time of about 68 s per block at d=768 from measured O(d^2) MSM scaling and a conservative 15-30x GPU-MSM speedup range based on Icicle's published 30x result for n >= 2^20 and extrapolated to the smaller-n regime. Privacy scope: NanoZK hides weights and activations from verifiers and auditors but does not hide the prompt from the prover; this is complementary to HE/MPC.
Dense retrieval services increasingly underpin semantic search, recommendation, and retrieval-augmented generation, yet clients typically receive only a top-$k$ list with no auditable evidence of how it was produced. We present V3DB, a verifiable, versioned vector-search service that enables audit-on-demand correctness checks for approximate nearest-neighbour (ANN) retrieval executed by a potentially untrusted service provider. V3DB commits to each corpus snapshot and standardises an IVF-PQ search pipeline into a fixed-shape, five-step query semantics. Given a public snapshot commitment and a query embedding, the service returns the top-$k$ payloads and, when challenged, produces a succinct zero-knowledge proof that the output is exactly the result of executing the published semantics on the committed snapshot -- without revealing the embedding corpus or private index contents. To make proving practical, V3DB avoids costly in-circuit sorting and random access by combining multiset equality/inclusion checks with lightweight boundary conditions. Our prototype implementation based on Plonky2 achieves up to $22\times$ faster proving and up to $40\%$ lower peak memory consumption than the circuit-only baseline, with millisecond-level verification time. Github Repo at https://github.com/TabibitoQZP/zk-IVF-PQ.
This chapter explores the intersection of data governance, data quality, and blockchain technology, presenting a paradigm shift from traditional centralized data management to decentralized architectures. As data solidifies its role as a critical asset, ensuring its integrity and trustworthiness has become paramount. We begin by establishing the principles of data governance and quality, highlighting the limitations of conventional systems that rely on trusted intermediaries, which introduce single points of failure and censorship risks. Blockchain technology emerges as a compelling alternative, offering a decentralized, immutable, and transparent ledger that fundamentally enhances data integrity and trust. Through an analysis of its core components – including cryptographic hashing, consensus mechanisms, and distributed networks – we examine the inherent advantages and disadvantages of blockchain. The chapter delves into the functional extensions of blockchain, such as smart contracts and Decentralized Autonomous Organizations (DAOs), which enable automated, transparent, and autonomous governance models. However, the transition to blockchain is not without its challenges. We critically assess issues of scalability, data privacy, the “oracle problem,” and the “garbage in, garbage out” principle, which persist in decentralized environments. The chapter concludes that the “quality” of blockchain as a data management solution is not absolute but is contingent upon the specific requirements of the use case, demanding a careful evaluation of its trade-offs.
Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate contributors for their resources and risks. Enabled by Web3 primitives, especially blockchains, recent FL proposals incorporate incentive mechanisms for open participation, yet most focus primarily on algorithmic design and overlook system-level challenges, including coordination efficiency, secure handling of model updates, and practical usability. We present FWeb3, a practical Web3-enabled FL framework for incentive-aware training in open environments. FWeb3 adopts a modular architecture that separates FL functions from Web3 support services, decoupling the off-chain training and data plane from on-chain settlement while preserving verifiable incentive execution. The framework supports pluggable aggregation and contribution evaluation methods and provides a browser-native DApp interface to lower the participation barrier. We evaluate FWeb3 in real-world settings and show that it supports end-to-end incentive-aware FL with transaction and data-transfer overheads of only 21.3% and 3.4% in WAN; FWeb3 also deploys from zero configuration in under 3 minutes and enables user onboarding in under 1 minute.
Oshani Seneviratne, Fernando Spadea, Adrien Pavao, Aaron Micah Green · 5 authors
Temporal Web analytics increasingly relies on large-scale, longitudinal data to understand how users, content, and systems evolve over time. A rapidly growing frontier is the \emph{Temporal Web3}: decentralized platforms whose behavior is recorded as immutable, time-stamped event streams. Despite the richness of this data, the field lacks shared, reproducible benchmarks that capture real-world temporal dynamics, specifically censoring and non-stationarity, across extended horizons. This absence slows methodological progress and limits the transfer of techniques between Web3 and broader Web domains. In this paper, we present the \textit{FinSurvival Challenge 2025} as a case study in benchmarking \emph{temporal Web3 intelligence}. Using 21.8 million transaction records from the Aave v3 protocol, the challenge operationalized 16 survival prediction tasks to model user behavior transitions.We detail the benchmark design and the winning solutions, highlighting how domain-aware temporal feature construction significantly outperformed generic modeling approaches. Furthermore, we distill lessons for next-generation temporal benchmarks, arguing that Web3 systems provide a high-fidelity sandbox for studying temporal challenges, such as churn, risk, and evolution that are fundamental to the wider Web.
We study what model-identifying information leaks through commercial language-model APIs that expose top-k token log probabilities. Building on extreme-value theory predictions for logit order-statistic gaps, we confirm that the normalized third logit gap (δ norm) remains near the Gumbel-class constant ≈0.318 across 6 models from 3 providers (OpenAI, Google Vertex AI, xAI) and 3 independent measurement sessions, demonstrating that output-layer universality persists through API truncation and quantization. We introduce a PPP-residualization transform that removes the dominant tail scale factor and reveals a low-dimensional but stable endpoint-specific geometry in the remaining gap spectrum. Contrary to common assumption, "provider" is not a geometrically coherent label: models do not cluster by corporate origin under these observables, but they do separate by model identity across independent sessions. Using a challenge-response protocol with centroid averaging and per-model thresholds, we demonstrate cross-session endpoint verification with a 0.83% breach rate (119/120 correct identifications across three temporal sessions); per-model thresholds eliminate all breaches on this dataset. We observe a robustness phase transition governed by enrollment depth. Under single-session enrollment, prompt selection is load-bearing: the majority of bootstrapped banks fail to separate the six endpoints. Under two-session enrollment, bank sensitivity collapses on this dataset, and a bank compiler produces small compiled banks that exceed the margin of larger uncompiled banks. A dimensionless robustness parameter SNR(K,S) unifies both axes: prompt count K and enrollment depth S jointly govern the transition from bank-sensitive to bank-robust verification. We discuss operational implications for re-enrollment cadence and template management in production deployments. Addendum (02/26/2026): Post-publication results extend this framework in two directions. A distillation experiment across six training protocols demonstrates that a model's structural fingerprint (weight-geometry regime) is completely invariant to knowledge distillation, while its functional fingerprint (PPP-residual template) converges 31--52% toward the teacher's — enabling forensic detection of distillation provenance through API measurements alone. A conditional impossibility theorem, machine-checked in Coq (41 theorems, 0 Admitted), proves that no standalone model can spoof another's PPP-residual template across independent challenge prompts without exhausting its KL divergence budget, under four explicit trust assumptions. Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
With the rapid advancement of Web 3.0 technologies, public blockchain platforms are witnessing the emergence of novel services designed to enhance user privacy and anonymity. However, the powerful untraceability features inherent in these services inadvertently make them attractive tools for criminals seeking to launder illicit funds. Notably, existing de-anonymization methods face three major challenges when dealing with such transactions: highly homogenized transactional semantics, limited ability to model temporal discontinuities, and insufficient consideration of structural sparsity in account association graphs. To address these, we propose GradWATCH, designed to track anonymous accounts in Ethereum privacy-preserving services. Specifically, we first design a learnable account feature mapping module to extract informative transactional semantics from raw on-chain data. We then incorporate transaction relations into the account association graph to alleviate the adverse effects of structural sparsity. To capture temporal evolution, we further propose an edge-aware sliding-window mechanism that propagates and updates gradients at three granularities. Finally, we identify accounts controlled by the same entity by measuring their embedding distances in the learned representation space. Experimental results show that even under the conditions of unbalanced labels and sparse transactions, GradWATCH still achieves significant performance gains, with relative improvements ranging from 1.62% to 15. 22% in the MRR and from 3. 85% to 7. 31% in the F_1.
Current approaches to verifying AI training data compliance face a fundamental tension: copyright holders need to know whether their content was used in training (EU AI Act, Article 53(1)(d)), while model providers need to protect their training data as trade secrets (GDPR, trade secret law). Existing zero-knowledge proof systems for machine learning (ZKML) address this partially by providing proofs of non-membership for exact data points. However, real-world training pipelines involve tokenization, chunking, paraphrasing, and augmentation, rendering exact-match proofs insufficient. We identify a gap in the literature: no existing system combines semantic fingerprinting with zero-knowledge proofs to enable semantic non-membership verification. We propose an architecture for Zero-Knowledge Semantic Non-Membership (ZK-SNM) that enables a model provider to prove, without revealing any training data, that no document in their training corpus is semantically similar to a queried document above a specified threshold. We discuss the technical challenges, including the computational cost of similarity search within ZK circuits, and propose mitigation strategies based on locality-sensitive hashing and hierarchical verification. This position paper establishes the problem formulation and proposed architecture; experimental validation is left to subsequent work.
André Augusto, Rafael Belchior, Jonas Pfannschmidt, André Vasconcelos · 5 authors
Cross-chain bridges are a blockchain interoperability middleware that supports the transfer of assets and data across blockchains. However, several of these bridges have vulnerabilities that have caused 3.2 billion dollars in losses since May 2021. Some studies have revealed the existence of these vulnerabilities, but there is little quantitative research available, and there are no safeguard mechanisms to protect bridges from such attacks. Furthermore, no studies are available on the practices of cross-chain bridges that can cause financial losses. We propose XChainWatcher (Cross-Chain Watcher), a modular and extensible logic-driven anomaly detector for cross-chain bridges. It operates in three main phases: (1) decoding events and transactions from multiple blockchains, (2) building logic relations from the extracted data, and (3) evaluating these relations against a set of detection rules. Using XChainWatcher, we analyze data from two previously attacked bridges: the Ronin and Nomad bridges. XChainWatcher successfully identified the transactions that led to losses of $611M and $190M (USD) and surpassed the results obtained by a reputable security firm in the latter. We not only uncover successful attacks, but also reveal other anomalies, such as 37 cross-chain transactions (cctx) that should not have accepted, failed attempts to exploit Nomad, over $7.8M worth of tokens locked on one chain but never released on Ethereum, and $200K lost by users due to inadequate interaction with bridges. We provide the first open dataset of 81,000 cctxs across three blockchains, capturing more than $4.2B in token transfers.
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.
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.