A big challenge posed in blockchain centric platforms is achieving scalability while also preserving user privacy. This report details the design, implementation and evaluation of a Layer-2 scaling solution for Hyperledger Fabric using Zero Knowledge Rollups (ZK Rollups). The proposed architecture introduces an off chain sequencer that accepts transactions immediately and sends them for batching into a Merkle tree based rollup, using ZK proofs to attest to the correctness and verifiability of the entire batch. The design aims to decouple transaction ingestion from actual on chain settlements to address Fabric scalability limitations and increase throughput under high load conditions. The baseline architecture in Hyperledger Fabric constrains transaction requests due to endorsement, ordering and validation phases, leading to a throughput of 5 to 7 TPS with an average latency of 4 seconds. Our Layer-2 solution achieves an ingestion throughput of 70 to 100 TPS, leading to an increase of nearly ten times due to the sequencer immediate acceptance of each transaction and reducing client perceived latency by nearly eighty percent to 700 to 1000 milliseconds. This work demonstrates that integrating ZK Rollups in Hyperledger Fabric enhances scalability while not compromising the security guarantees of a permissioned blockchain network.
We present SPEAR, a multi-agent coordination framework for smart contract auditing that applies established MAS patterns in a realistic security analysis workflow. SPEAR models auditing as a coordinated mission carried out by specialized agents: a Planning Agent prioritizes contracts using risk-aware heuristics, an Execution Agent allocates tasks via the Contract Net protocol, and a Repair Agent autonomously recovers from brittle generated artifacts using a programmatic-first repair policy. Agents maintain local beliefs updated through AGM-compliant revision, coordinate via negotiation and auction protocols, and revise plans as new information becomes available. An empirical study compares the multi-agent design with centralized and pipeline-based alternatives under controlled failure scenarios, focusing on coordination, recovery behavior, and resource use.
Visual social media platforms have become primary venues for political discourse, yet we know little about how moralization operates differently across platforms and topics. Analyzing 2,027,595 TikToks and 1,126,972 Instagram posts during the 2024 US presidential election, we demonstrate that issues are not necessarily inherently moralized, but a product of audience demographics, platform architecture, and partisan framing. Using temporal supply-demand analysis and moral foundations scoring (eMFD), we examine the dynamics of key electoral issues. Three key findings emerge. First, moralization patterns diverge dramatically by platform: TikTok's algorithm enabled viral spread of moralized abortion and immigration content despite lower supply, while Instagram amplified economic discourse that aligned supply and demand. Second, traditionally "pragmatic" economic issues became moralized-cryptocurrency discourse invoked loyalty and authority foundations more strongly than any other topic, framing regulation as government overreach. Third, platforms responded to different events: TikTok surged after Harris's nomination across all topics (96% reduction in supply volatility), while Instagram spiked around cryptocurrency policy developments. Semantic network analysis reveals TikTok's circular topology enables cross-cutting exposure while Instagram's fragmented structure isolates Harris from economic discourse. These findings demonstrate that understanding political moralization requires examining platform-specific ecosystems where architecture, demographics, and content strategy interact to determine which issues get moralized and how moral content spreads.
Decentralized Identifiers (DIDs) are increasingly deployed on distributed ledgers, yet systematic cross-platform evidence on their operational behavior remains limited. We present an empirical benchmarking study of three prominent ledger-based DID methods - Ethereum, Hedera, and XRP Ledger - using reference Software Development Kits (SDKs) under a unified experimental setup. We measure latency, transaction cost, and on-chain metadata exposure, normalizing latency by each platform's block or consensus interval and cost by its native value transfer fee. Privacy leakage is quantified using a Metadata-Leakage Score (MLS), an entropy-based measure expressed in bits per operation. Our results reveal distinct architectural trade-offs. Ethereum enables near-instant, off-chain DID creation, but incurs the highest latency and cost for on-chain lifecycle operations. XRPL delivers deterministic and stable latency with fixed, low fees, yet exhibits higher metadata leakage due to more verbose transaction payloads. Hedera achieves the lowest on-chain latency and low fees with minimal metadata leakage, while occasional variance arises from SDK-side processing and confirmation pipelines. Overall, the findings show that ledger architecture and SDK workflows play a major role in shaping DID latency, cost, and metadata exposure, complementing the effects of the underlying consensus mechanism. These results provide evidence-based insights to support informed selection and configuration of DID systems under performance and privacy constraints.
Nacereddine Sitouah, Francesco Bruschi, Stefano De Cillis
The passing of the eIDAS amendment marks an important milestone for EU countries and changes how they must manage digital credentials for both public services and businesses. Italy has led in adopting eIDAS, first with CIE and SPID identity schemes, and now with the Italian Wallet (IO app) aligned to eIDAS 2.0. Self-Sovereign Identity (SSI) is a decentralized model born from the success of Distributed Ledgers, giving individuals full control over their digital identity. The current eIDAS 2.0 and its implementation acts diverge from SSI principles, rendering the European Digital Identity Wallet (EUDIW) centralized and merely user-centric, prioritizing security and legal protection over true self-sovereignty. This paper proposes an architecture that enables the use of IT Wallet credentials and services in an SSI-compliant environment through Trusted Execution Environments and Zero-Knowledge Proofs.
Gian Sebastian Mier Bello, Alexander Martinez Mendez, Carlos J. Barrios H., Robinson Rivas · 5 authors
In most High Performance Computing (HPC) projects nowadays, there is a lot of data obtained from different sources, depending on the project's objectives. Some of that data is very huge in terms of size, so copying such data sometimes is an unrealistic goal. On the other hand, science requires data used for different purposes to remain unaltered, so different groups of researchers can reproduce results, discuss theories, and validate each other. In this paper, we present a novel approach to help research groups to validate data integrity on such distributed repositories using Blockchain. Originally developed for cryptographic currencies, Blockchain has demonstrated a versatile range of uses. Our proposal ensures 1) secure access to data management, 2) easy validation of data integrity, and 3) an easy way to add new records to the dataset with the same robust integrity policy. A prototype was developed and tested using a subset of a public dataset from a real scientific collaboration, the Latin American Giant Observatory (LAGO) Project.
This paper documents the installation, configuration, and operation of a full Bitcoin node in a Linux environment, from manual compilation of the source code to complete synchronization with the network. The technical phases of the process are described, the main files generated by Bitcoin Core are analyzed, and the effects of the parameters txindex, prune, dbcache, maxmempool, and maxconnections are empirically studied. System resources during the block download (IBD) mechanism are also documented, and the operational importance of each resource is explained. This paper provides a solid foundation for future research proposals on Bitcoin node performance or for the development of blockchain data query tools.
Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-off between convenience and privacy by routing sensitive financial data through centralized servers, increasing security risks, and diminishing user trust. This study introduces a device-native autonomous Artificial Intelligence (AI) agent system for privacy-preserving negotiations. The proposed system operates exclusively on user hardware, enabling real-time bargaining while maintaining sensitive constraints locally. It integrates zero-knowledge proofs to ensure privacy and employs distilled world models to support advanced on-device reasoning. The architecture incorporates six technical components within an agentic AI workflow. Agents autonomously plan negotiation strategies, conduct secure multi-party bargaining, and generate cryptographic audit trails without exposing user data to external servers. The system is evaluated in insurance and B2B procurement scenarios across diverse device configurations. Results show an average success rate of 87%, a 2.4x latency improvement over cloud baselines, and strong privacy preservation through zero-knowledge proofs. User studies show 27% higher trust scores when decision trails are available. These findings establish a foundation for trustworthy autonomous agents in privacy-sensitive financial domains.
Self-Sovereign Identity is a transformative paradigm in digital identity management, empowering individuals with full control over their credentials. However, the coexistence of diverse SSI ecosystems, such as the European Digital Identity and the European Blockchain Services Infrastructure, poses significant challenges for cross-ecosystem interoperability due to technological and trust framework differences. This paper introduces \textit{interID}, a modular credential verification application that addresses this fragmentation by orchestrating ecosystem-specific verifier services. Our key contributions include: (1) an ecosystem-agnostic orchestration layer that interfaces with multiple SSI verification services, (2) a unified API that abstracts underlying protocol complexities for service providers, and (3) a practical implementation that bridges three major SSI ecosystems: Hyperledger Indy/Aries, EBSI, and EUDI. Evaluation results demonstrate that interID successfully verifies credentials across all tested wallets with minimal performance overhead, while maintaining a flexible architecture that can be extended to accept credentials from additional SSI ecosystems. This work offers both a technical solution and architectural pattern for achieving interoperability in SSI verifier implementations.
Damilare Peter Oyinloye, Mohd Sameen Chishti, Jingyue Li
Single-bridge blockchain solutions enable cross-chain communication. However, they are associated with centralization and single-point-of-failure risks. This paper proposes Proof of Success and Reward Distribution (PSCRD), a novel multi-bridge response coordination and incentive distribution protocol designed to address the challenges. PSCRD introduces a fair reward distribution system that equitably distributes the transfer fee among participating bridges, incentivizing honest behavior and sustained commitment. The purpose is to encourage bridge participation for higher decentralization and lower single-point-of-failure risks. The mathematical analysis and simulation results validate the effectiveness of PSCRD using two key metrics: the Gini index, which demonstrates a progressive improvement in the fairness of the reward distribution as new bridge groups joined the network; and the Nakamoto coefficient, which shows a significant improvement in decentralization over time. These findings highlight that PSCRD provides a more resilient and secure cross-chain bridge system without substantially increasing user costs.
Andrea Venturi, Imanol Jerico-Yoldi, Francesco Zola, Raul Orduna
As Law Enforcement Agencies advance in cryptocurrency forensics, criminal actors aiming to conceal illicit fund movements increasingly turn to "mixin" services or privacy-based cryptocurrencies. Monero stands out as a leading choice due to its strong privacy preserving and untraceability properties, making conventional blockchain analysis ineffective. Understanding the behavior and operational patterns of criminal actors within Monero is therefore challenging and it is essential to support future investigative strategies and disrupt illicit activities. In this work, we propose a case study in which we leverage a novel graph-based methodology to extract structural and temporal patterns from Monero transactions linked to already discovered criminal activities. By building Address-Ring-Transaction graphs from flagged transactions, we extract structural and temporal features and use them to train Machine Learning models capable of detecting similar behavioral patterns that could highlight criminal modus operandi. This represents a first partial step toward developing analytical tools that support investigative efforts in privacy-preserving blockchain ecosystems
Software services are crucial for reliable communication and networking; therefore, Site Reliability Engineering (SRE) is important to ensure these systems stay reliable and perform well in cloud-native environments. SRE leverages tools like Prometheus and Grafana to monitor system metrics, defining critical Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for maintaining high service standards. However, a significant challenge arises as many developers often lack in-depth understanding of these tools and the intricacies involved in defining appropriate SLIs and SLOs. To bridge this gap, we propose a novel SRE platform, called SRE-Llama, enhanced by Generative-AI, Federated Learning, Blockchain, and Non-Fungible Tokens (NFTs). This platform aims to automate and simplify the process of monitoring, SLI/SLO generation, and alert management, offering ease in accessibility and efficy for developers. The system operates by capturing metrics from cloud-native services and storing them in a time-series database, like Prometheus and Mimir. Utilizing this stored data, our platform employs Federated Learning models to identify the most relevant and impactful SLI metrics for different services and SLOs, addressing concerns around data privacy. Subsequently, fine-tuned Meta's Llama-3 LLM is adopted to intelligently generate SLIs, SLOs, error budgets, and associated alerting mechanisms based on these identified SLI metrics. A unique aspect of our platform is the encoding of generated SLIs and SLOs as NFT objects, which are then stored on a Blockchain. This feature provides immutable record-keeping and facilitates easy verification and auditing of the SRE metrics and objectives. The automation of the proposed platform is governed by the blockchain smart contracts. The proposed SRE-Llama platform prototype has been implemented with a use case featuring a customized Open5GS 5G Core.
Shashank Motepalli, N. Garg, Gengrui Zhang, Hans‐Arno Jacobsen
Geospatial decentralization is essential for blockchains, ensuring regulatory resilience, robustness, and fairness. We empirically analyze five major Proof of Stake (PoS) blockchains: Aptos, Avalanche, Ethereum, Solana, and Sui, revealing that a few geographic regions dominate consensus voting power, resulting in limited geospatial decentralization. To address this, we propose Geospatially aware Proof of Stake (GPoS), which integrates geospatial diversity with stake-based voting power. Experimental evaluation demonstrates an average 45% improvement in geospatial decentralization, as measured by the Gini coefficient of Eigenvector centrality, while incurring minimal performance overhead in BFT protocols, including HotStuff and CometBFT. These results demonstrate that GPoS can improve geospatial decentralization {while, in our experiments, incurring minimal overhead} to consensus performance.
Raffaele Cristodaro, Benjamin Kraner, Claudio J. Tessone
Tornado Cash is a decentralised mixer that uses cryptographic techniques to sever the on-chain trail between depositors and withdrawers. In practice, however, its anonymity can be undermined by user behaviour and operational quirks. We conduct the first cross-chain empirical study of Tornado Cash activity on Ethereum, BNB Smart Chain, and Polygon, introducing three clustering heuristics-(i) address-reuse, (ii) transactional-linkage, and (iii) a novel first-in-first-out (FIFO) temporal-matching rule. Together, these heuristics reconnect deposits to withdrawals and deanonymise a substantial share of recipients. Our analysis shows that 5.1 - 12.6% of withdrawals can already be traced to their originating deposits through address reuse and transactional linkage heuristics. Adding our novel First-In-First-Out (FIFO) temporal-matching heuristic lifts the linkage rate by a further 15 - 22 percentage points. Statistical tests confirm that these FIFO matches are highly unlikely to occur by chance. Comparable leakage across Ethereum, BNB Smart Chain, and Polygon indicates chain-agnostic user misbehaviour, rather than chain-specific protocol flaws. These results expose how quickly cryptographic guarantees can unravel in everyday use, underscoring the need for both disciplined user behaviour and privacy-aware protocol design. In total, our heuristics link over $2.3 billion in Tornado Cash withdrawals to identifiable deposits, exposing significant cracks in practical anonymity.
Alison Gonçalves Schemitt, Henrique Fan da Silva, Roben Castagna Lunardi, Diego Kreutz · 6 authors
The advent of quantum computing poses a threat to the security of traditional encryption algorithms. This has motivated the development of post-quantum cryptography (PQC). In 2024, the National Institute of Standards and Technology (NIST) standardized several PQC algorithms, marking an important milestone in the transition toward quantum-resistant security. Blockchain systems fundamentally rely on cryptographic primitives to guarantee data integrity and transaction authenticity. However, widely used algorithms such as ECDSA, employed in Bitcoin, Ethereum, and other networks, are vulnerable to quantum attacks. Although adopting PQC is essential for long-term security, its computational overhead in blockchain environments remains largely unexplored. In this work, we propose a methodology for benchmarking both PQC and traditional cryptographic algorithms in blockchain contexts. We measure signature generation and verification times across diverse computational environments and simulate their impact at scale. Our evaluation focuses on PQC digital signature schemes (ML-DSA, Dilithium, Falcon, Mayo, SLH-DSA, SPHINCS+, and Cross) across security levels 1 to 5, comparing them to ECDSA, the current standard in Bitcoin and Ethereum. Our results indicate that PQC algorithms introduce only minor performance overhead at security level 1, while in some scenarios they significantly outperform ECDSA at higher security levels. For instance, ML-DSA achieves a verification time of 0.14 ms on an ARM-based laptop at level 5, compared to 0.88 ms for ECDSA. We also provide an open-source implementation to ensure reproducibility and to encourage further research.
Decentralized data-feed systems enable blockchain-based smart contracts to access off-chain information by aggregating values from multiple oracles. To improve accuracy, these systems typically use an aggregation function, such as majority voting, to consolidate the inputs they receive from oracles and make a decision. Depending on the final decision and the values reported by the oracles, the participating oracles are compensated through shared rewards. However, such incentive mechanisms are vulnerable to mirroring attacks, where a single user controls multiple oracles to bias the decision of the aggregation function and maximize rewards. This paper analyzes the impact of mirroring attacks on the reliability and dependability of majority voting-based data-feed systems. We demonstrate how existing incentive mechanisms can unintentionally encourage rational users to implement such attacks. To address this, we propose a new incentive mechanism that discourages Sybil behavior. We prove that the proposed mechanism leads to a Nash Equilibrium in which each user operates only one oracle. Finally, we discuss the practical implementation of the proposed incentive mechanism and provide numerical examples to demonstrate its effectiveness.
The construction industry faces significant challenges regarding material waste and sustainable practices, necessitating innovative solutions that integrate automation, traceability, and decentralised decision-making to enable efficient material reuse. This paper presents a blockchain-enabled digital marketplace for sustainable construction material reuse, ensuring transparency and traceability using InterPlanetary File System (IPFS). The proposed framework enhances trust and accountability in material exchange, addressing key challenges in industrial automation and circular supply chains. A framework has been developed to demonstrate the operational processes of the marketplace, illustrating its practical application and effectiveness. Our contributions show how the marketplace can facilitate the efficient and trustworthy exchange of reusable materials, representing a substantial step towards more sustainable construction practices.
Ethereum smart contracts hold tens of billions of USD in DeFi and NFTs, yet comprehensive security analysis remains difficult due to unverified code, proxy-based architectures, and the reliance on manual inspection of complex execution traces. Existing approaches fall into two main categories: anomaly transaction detection, which flags suspicious transactions but offers limited insight into specific attack strategies hidden in execution traces inside transactions, and code vulnerability detection, which cannot analyze unverified contracts and struggles to show how identified flaws are exploited in real incidents. As a result, analysts must still manually align transaction traces with contract code to reconstruct attack scenarios and conduct forensics. To address this gap, TraceLLM is proposed as a framework that leverages LLMs to integrate execution trace-level detection with decompiled contract code. We introduce a new anomaly execution path identification algorithm and an LLM-refined decompile tool to identify vulnerable functions and provide explicit attack paths to LLM. TraceLLM establishes the first benchmark for joint trace and contract code-driven security analysis. For comparison, proxy baselines are created by jointly transmitting the results of three representative code analysis along with raw traces to LLM. TraceLLM identifies attacker and victim addresses with 85.19\% precision and produces automated reports with 70.37\% factual precision across 27 cases with ground truth expert reports, achieving 25.93\% higher accuracy than the best baseline. Moreover, across 148 real-world Ethereum incidents, TraceLLM automatically generates reports with 66.22\% expert-verified accuracy, demonstrating strong generalizability.
Yagmur Yigit, Mehmet Ali Erturk, Kerem Gursu, Berk Canberk
Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and verifiable physical-digital binding, making them unsuitable for latency-sensitive and distributed deployments. To address this gap, we introduce PRZK-Bind, a lightweight and decentralised authentication protocol that combines Schnorr-based zero-knowledge proofs with elliptic curve cryptography to establish secure, real-time correspondence between physical entities and DTs without relying on pre-shared secrets. Simulation results show that PRZK-Bind significantly improves performance, offering up to 4.5 times lower latency and 4 times reduced energy consumption compared to cryptography-heavy baselines, while maintaining false acceptance rates more than 10 times lower. These findings highlight its suitability for future smart city deployments requiring efficient, resilient, and trustworthy DT authentication.
Benjamin Kraner, Luca Pennella, Nicolò Vallarano, Claudio J. Tessone
We introduce a micro-velocity framework for analysing the on-chain circulation of Lidos liquid-staking tokens, stETH, and its wrapped ERC-20 form, wstETH. By reconstructing full transfer and share-based accounting histories, we compute address-level velocities and decompose them into behavioural components. Despite their growing importance, the micro-level monetary dynamics of LSTs remain largely unexplored. Our data reveal persistently high velocity for both tokens, reflecting intensive reuse within DeFi. Yet activity is highly concentrated: a small cohort of large addresses, likely institutional accounts, are responsible for most turnover, while the rest of the users remain largely passive. We also observe a gradual transition in user behavior, characterized by a shift toward wstETH, the non-rebasing variant of stETH. This shift appears to align with DeFi composability trends, as wstETH is more frequently deployed across protocols such as AAVE, Spark, Balancer, and SkyMoney. To make the study fully reproducible, we release (i) an open-source pipeline that indexes event logs and historical contract state, and (ii) two public datasets containing every Transfer and TransferShares record for stETH and wstETH through 2024-11-08. This is the first large-scale empirical characterisation of liquid-staking token circulation. Our approach offers a scalable template for monitoring staking asset flows and provides new, open-access resources to the research community.
The global financial system stands at an inflection point. Stablecoins represent the most significant evolution in banking since the abandonment of the gold standard, positioned to enable "Banking 2.0" by seamlessly integrating cryptocurrency innovation with traditional finance infrastructure. This transformation rivals artificial intelligence as the next major disruptor in the financial sector. Modern fiat currencies derive value entirely from institutional trust rather than physical backing, creating vulnerabilities that stablecoins address through enhanced stability, reduced fraud risk, and unified global transactions that transcend national boundaries. Recent developments demonstrate accelerating institutional adoption: landmark U.S. legislation including the GENIUS Act of 2025, strategic industry pivots from major players like JPMorgan's crypto-backed loan initiatives, and PayPal's comprehensive "Pay with Crypto" service. Widespread stablecoin implementation addresses critical macroeconomic imbalances, particularly the inflation-productivity gap plaguing modern monetary systems, through more robust and diversified backing mechanisms. Furthermore, stablecoins facilitate deregulation and efficiency gains, paving the way for a more interconnected international financial system. This whitepaper comprehensively explores how stablecoins are poised to reshape banking, supported by real-world examples, current market data, and analysis of their transformative potential.
We argue that the technical foundations of non-fungible tokens (NFTs) remain inadequately understood. Prior research has focused on market dynamics, user behavior, and isolated security incidents, yet systematic analysis of the standards underpinning NFT functionality is largely absent. We present the first study of NFTs through the lens of Ethereum Improvement Proposals (EIPs). We conduct a large-scale empirical analysis of 191 NFT-related EIPs and 10K+ Ethereum Magicians discussions (as of July, 2025). We integrate multi-dimensional analyses including the automated parsing of Solidity interfaces, graph-based modeling of inheritance structures, contributor profiling, and mining of community discussion data. We distinguish foundational from emerging standards, expose poor cross-version interoperability, and show that growing functional complexity heightens security risks.
Smart contracts are important for digital finance, yet they are hard to patch once deployed. Prior work has mainly explored LLMs for smart contract vulnerability detection, leaving end-to-end automated exploit generation (AEG) much less understood. We study that gap with \textsc{ReX}, an execution-grounded framework that links LLM-based exploit synthesis to the Foundry stack for end-to-end generation, compilation, execution, and validation. Five recent LLMs are evaluated across eight common vulnerability classes, supported by a curated dataset of 38{+} real incident PoCs and three automation aids: prompt refactoring, a compiler feedback loop, and templated test harnesses. Results indicate that current frontier LLMs can often produce deterministic PoCs for single-contract vulnerabilities, but remain weak on cross-contract attacks; outcomes depend mainly on the model and bug type, while code structure and prompt tuning contribute less in our setting. The study also surfaces important boundary conditions of LLM-driven AEG, including gaps between oracle-validated exploitability and real-world economic attacks, pointing to the need for stronger defenses and more realistic evaluation.
Burak Arda Okutan, Stefan Schmid, Yvonne-Anne Pignolet
Decentralized autonomous organizations (DAOs) rely on governance mechanism without centralized leadership. This paper presents an empirical study of user behavior in governance for a variety of DAOs, ranging from DeFi to gaming, using the Internet Computer Protocol DAO framework called SNS (Service Nervous System). To analyse user engagement, we measure participation rates and frequency of proposals submission and voter approval rates. We evaluate decision duration times to determine DAO agility. To investigate dynamic aspects, we also measure metric shifts in time. We evaluate over 3,000 proposals submitted in a time frame of 20 months from 14 SNS DAOs. The selected DAO have been existing between 6 and 20 months and cover a wide spectrum of use cases, treasury sizes, and number of participants. We also compare our results for SNS DAOs with DAOs from other blockchain platforms. While approval rates are generally high for all DAOs studied, SNS DAOs show slightly more alignment. We observe that the SNS governance mechanisms and processes in ICP lead to higher activity, lower costs and faster decisions. Most importantly, in contrast to studies which report a decline in participation over time for other frameworks, SNS DAOs exhibit sustained or increasing engagement levels over time.