Blockchain technology, also known as Distributed Ledger Technology (DLT), is a decentralized, distributed ledger that chains encrypted blocks of data together. Its inherent properties, such as transparency, data integrity, and security measures, make it a prominent technology for various industries. Consensus protocols, hashing, and smart contracts enhance its security and reliability. However, scalability has proven to be a significant challenge in implementing public blockchains. The rapid growth of network nodes and miners' transaction calculations are the main causes of scalability issues. Efficient data scalability is crucial, as it can result in reduced maintenance costs, improved user experience, and higher agility. In this paper, we explore methods to enhance the consensus algorithm and compare its performance with existing algorithms.
LOULID Adil -, GADMI Mariam -, LOTFI Siham, BEN DARKAWI Zakaria -
This paper explores the evolving landscape of SME financing in a context marked by the progressive tightening of traditional bank credit and the emergence of innovative funding alternatives. Small and medium-sized enterprises (SMEs), widely recognized as key drivers of innovation and employment, face increasing difficulties in accessing conventional financial resources due to heightened risk aversion among banks, stricter regulatory requirements, and macroeconomic instability. In response, SMEs are progressively turning to alternative financing solutions, such as crowdfunding, venture capital, peer-to-peer lending, and blockchain-based mechanisms, including smart contracts. The study highlights the dual dynamics shaping the current financing environment: while traditional sources like bank credit and government grants remain essential, they are no longer sufficient on their own. New technologies and decentralized platforms are redefining the financial ecosystem, offering greater flexibility, transparency, and inclusion. However, these alternatives also come with challenges, such as regulatory uncertainty, market saturation, and the need for strategic adaptation. Through a comparative and analytical approach, the paper underscores the importance of fostering a diversified, resilient, and innovation-oriented financial framework. It calls for coordinated efforts between public policy, financial institutions, and technological actors to support the sustainable development and competitiveness of SMEs in an increasingly complex economic environment.
Conventional cryptographic approaches face increasing insecurity because quantum computing and attacks enabled by AI operate at a rapid speed of technological advancement. The review analyzes IEEE research activities on progressive cryptographic methods consisting of post-quantum cryptography (PQC) and fully homomorphic encryption (FHE) in addition to zero-knowledge proofs (ZKP) and AI-augmented cryptographic models. We explore both advantages and limitations in addition to applicable uses for protecting modern digital frameworks which include blockchain, IoT and 5G networks. Standardization efforts as well as hybrid crypto framework trends receive analysis in the article to establish long-lasting secure systems.
L. Domven, Aliyu Danladi Hina, A. M. Kwami, C. M. Miri · 5 authors
This study proposes a secure mobile voting system that integrates elliptic curve cryptography (ECC) with secure multiparty computation (SMPC) to guarantee vote confidentiality, integrity, and verifiability. Designed to enable scalable, privacy-preserving elections via mobile devices, the system authenticates voters using registered numbers and records ballots as encrypted points on an elliptic curve. Encrypted votes are published on a public bulletin board alongside zero-knowledge proofs to ensure their validity. To safeguard decryption, Shamir’s secret sharing distributes keys among trusted authorities, enabling collective tallying without exposing individual votes. The system incorporates ECC-based secret sharing, homomorphic encryption, and zero-knowledge proofs, leveraging the hardness of the elliptic curve discrete logarithm problem (ECDLP) for robust security. Both experimental and theoretical evaluations demonstrate that ECC significantly improves computational efficiency and scalability, making the system well-suited for resource-constrained environments. Overall, the integration of ECC and SMPC offers a practical, efficient, and secure framework for mobile elections, effectively balancing privacy, security, and performance.
The combination of voting bond financing transparency issues and misappropriation issues along with insufficient financial responsibility oversight has exposed the requirement for advanced transparent funding solutions. ElectraChain offers blockchain technology that develops a new political funding structure which maintains donation safety alongside full transparency and donor identity protection. ElectraChain uses Distributed Ledger Technology (DLT) in order to establish a tamper-proof decentralized system for transaction recording which surpasses corrupted conventional systems that allow selective disclosure. This paper explains the fundamental framework alongside technological infrastructure that ElectraChain adopts along with its consequences on policy making through an analysis of its election bond monitoring capacity. This study aims to create progress in electoral financing accountability by uniting privacy protection with transparency oversight in order to enhance democratic institution integrity.
With the emergence of blockchain technology, many firms have approached cryptocurrency by allowing their customers to use it as a form of payment. However, little research has examined firms’ acceptance of cryptocurrency, the unique strategies that have been undertaken, or the impact on firm-level outcomes. Drawing on signaling theory, the author applies the event study methodology to learn how firm value (i.e., stock price) is impacted by firms’ announcement of cryptocurrency acceptance. The author finds that, on average, firms have lost 2.73% in firm value as a result of announcing cryptocurrency acceptance. However, a moderation analysis reveals that firms that have chosen to accept Bitcoin (as opposed to focusing exclusively on alternative cryptocurrencies) and firms that have approached cryptocurrency acceptance in more recent years experience financial gains. The geographical location (i.e., domestic or international) of cryptocurrency acceptance is not found to have a moderating impact.
The full-scale war in Ukraine has exposed critical vulnerabilities in centralized energy grids, driving the urgent need for decentralized renewable energy solutions. This study investigates the economic efficiency of state financial and investment support for the advancement of distributed green energy systems in Ukraine, particularly through concessional financing initiatives such as the "5-7-9" program. The decision-making analysis focuses on small and medium-sized enterprises investing in 10-, 20-, and 30-kW hybrid wind-solar photovoltaic systems accompanied by storage facilities. Financial viability was assessed using key indicators, including Levelized Cost of Energy, Net Present Value, Internal Rate of Return, Profitability Index, and Discounted Payback Period. Results indicate that with preferential financing, the considered projects achieved strong economic performance, while traditional commercial loans offered by commercial banks rendered small-scale decentralized renewable energy solutions financially unfeasible. Based on this, it has been demonstrated that strategic public-private collaboration and effective financial policy frameworks are critical for scaling renewable energy adoption and accelerating Ukraine’s green and digital transition. The article presents developed strategies and a roadmap for integrating decentralized power systems into Ukraine’s digital economy, which, during and after the war, will help strengthen energy resilience, reduce operational risks, and foster the country’s sustainable growth. However, limitations include assumptions of stable macroeconomic conditions and a focus solely on internal energy consumption. Future research should investigate tailored financial mechanisms for different business types and explore the broader socio-economic impacts of investments in decentralized green power systems, as well as the sensitivity of projects’ economic indicators for optimal decision-making.
Omar Jarkas, Ryan K. L. Ko, Naipeng Dong, Redowan Mahmud
Integrity verification and attestation are critical in containerized environments, where traditional Linux Integrity Measurement Architecture (IMA) falls short due to its lack of container-specific contextualization. These gaps undermine container autonomy, escalate privacy risks, and impede granular integrity checks. Addressing these challenges, this paper introduces the Virtual IMA (VIMA), a novel framework that refines Linux IMA’s principles to support containerized settings. Using nested Merkle trees, VIMA’s Two-Tree Architecture (2TA) enables detailed integrity assessments across system-wide monolithic trees and individual container trees. Integrating Merkle and zero-knowledge (ZK) proofs establishes VIMA as a secure, privacy-preserving verification and attestation solution. Our comparative analysis and initial prototype testing reveal that VIMA significantly improves upon traditional IMA with minimal performance overhead, offering substantial scope for optimization.
Hao Cheng, Georgios Fotiadis, Johann Großschädl, Daniel Page
Non-degenerate bilinear maps on elliptic curves, commonly referred to as pairings, have many applications including short signature schemes, zero-knowledge proofs and remote attestation protocols. Computing a state-of-the-art pairing at the 128-bit security level, such as the optimal ate pairing over the curve BLS12-381, is very costly due to the high complexity of some of its sub-operations: most notable are the Miller loop and final exponentiation. In the past ten years, a few optimized pairing implementations have been introduced in the literature, but none of those took advantage of the vector (SIMD) extensions of state-of-the-art Intel and AMD CPUs, especially AVX-512; this is surprising, because doing so offers the potential to reach significant speed-ups. Consequently, the questions of 1) how computation of the optimal ate pairing can be effectively vectorized, and 2) what execution time such a vectorized implementation can achieve are still open. This paper addresses said questions by introducing a carefully-optimized AVX-512 implementation of the optimal ate pairing on BLS12-381. A central feature of the implementation is the use of 8-way Integer Fused Multiply-Add (IFMA) instructions, which are capable to execute eight 52 x 52-bit multiplications in a SIMD-parallel fashion. We introduce new vectorization strategies and describe optimizations of existing ones to speed up arithmetic operations in the extension fields Fp4 , Fp6 , and Fp12 as well as certain higher-level functions. Furthermore, we discuss some parallelization bottlenecks and how they impact execution time. We benchmarked our pairing software, which we call avxbls, on an Intel Core i3-1005G1 (“Ice Lake”) CPU and found that it needs 1, 265, 314 clock cycles (resp. 1, 195, 236 clock cycles) for the full pairing, with the Granger-Scott cyclotomic squaring (resp. compressed cyclotomic squaring) being used in the final exponentiation. For comparison, the non-vectorized (i.e., scalar) x64 assembly implementation from the widely-used blst library has an execution time of 2, 351, 615 cycles, which is 1.86 times (resp. 1.97 times) slower. avxbls also outperforms Longa’s implementation (CHES 2023) by almost the same factor. The practical importance of these results is amplified by Intel’s recent announcement to support AVX10, which includes IFMA instructions, in all future CPUs.
Thibauld Feneuil, Matthieu Rivain, Auguste Warmé-Janville
Side-channel attacks pose significant threats to cryptographic implementations, which require the inclusion of countermeasures to mitigate these attacks. In this work, we study the masking of state-of-the-art post-quantum signatures based on the MPC-in-the-head paradigm. More precisely, we focus on the recent threshold-computation-in-the-head (TCitH) framework that applies to some NIST candidates of the post-quantum standardization process. We first provide an analysis of side-channel attack paths in the signature algorithms based on the TCitH framework. We then explain how to apply standard masking to achieve a d-probing secure implementation of such schemes, with performance scaling in O(d2), for d the masking order.Our main contribution is to introduce different ways to tweak those signature schemes towards their masking friendliness. While the TCitH framework comes in two variants, the GGM variant and the Merkle tree variant, we introduce a specific tweak for each of these variants. These tweaks allow us to achieve complexities of O(d) and O(d log d) at the cost of non-constant signature size, caused by the inclusion of additional seeds in the signature. We also propose a third tweak that takes advantage of the threshold secret sharing used in TCitH. With the right choice of parameters, we show how, by design, some parts of the TCitH algorithms satisfy probing security without additional countermeasures. While this approach can substantially reduce the cost of masking in some part of the signature algorithm, it degrades the soundness of the core zero-knowledge proof, hence slightly increasing the size of the signature.We analyze the complexity of the masked implementations of our tweaked TCitH signatures and provide benchmarks on a RISC-V platform with built-in hash accelerator. We use a modular benchmarking approach, allowing to estimate the performance of diverse signature instances with different tweaks and parameters. Our results illustrate how the different variants scale for an increasing masking order. For instance, for a masking order d = 3, we obtain signatures of around 14 kB that run in 0.67 second on a the target RISC-V CPU with a 250MHz frequency. This is to be compared with the 4.7 seconds required by the original signature scheme masked at the same order on the same platform. For a masking order d = 7, we obtain a signature of 17.5 kB running in 1.75 second, to be compared with 16 seconds for the stardard masked signature.Finally, we discuss the extension of our techniques to signature schemes based on the VOLE-in-the-Head framework, which shares similarities with the GGM variant of TCitH. One key takeaway of our work is that the Merkle tree variant of TCitH is inherently more amenable to efficient masking than frameworks based on GGM trees, such as TCitH-GGM or VOLE-in-the-Head.
From the earliest markets to today’s globally interconnected economies, exchanging money has been a defining feature of civilization. While historically centralized institutions have been in charge of securing and finalizing transactions, the advent of Bitcoin has marked the birth of decentralized finance. In this new paradigm, trust is no longer placed in a specific government, institution or corporation, but instead on mathematical proofs, protocol design and cryptographic assumptions. In an ever increasingly multipolar world - where financial censorship and transaction surveillance are becoming common coercion tools - the appeal of uncensorable money continues to grow. While a variety of decentralized financial systems exist, their interoperability remains a critical challenge. In this thesis, we study existing solutions and propose new methods for cross-system assets exchange. We first examine how to realize secure peer-to-peer (P2P) asset exchange protocols between two users. Such a task can be accomplished by a class of blockchain protocols known as atomic swaps, and we highlight the limitations of solutions that either require the underlying blockchain to support scripting or rely on a computionally intensive cryptographic primitive known as timelock puzzles. In order to improve on the current state of the art, we identify and propose a natural and minimal blockchain functionality called commit transactions, which we show to be sufficient to realize generic atomic swaps protocols. We next investigate how multiple users can establish a decentralized exchange service. Building on top of the traditional liquidity pool setting, we describe a system that extends to a cross-chain environment. We provide a costruction that, under an assumed setup mechanism, realizes an universal exchange protocol. Finally, we explore how such solutions can be realized in the most challenging setting of private and anonymous systems. Specifically, we focus into achieving compability with the most commonly traded private cryptocurrency, Monero. We propose a modifications to adapt Monero’s transaction scheme, ring confidential transactions (RingCT), to the newly proposed atomic swap protocol.
Florian Hirner, Florian Krieger, Constantin Piber, Sujoy Sinha Roy
Zero-knowledge proofs (ZKPs) are cryptographic protocols that enable one party to prove the validity of a statement without revealing any information beyond its truth. Central building blocks in many ZKPs are polynomial commitment schemes (PCS) where constructions with linear-time provers are especially attractive. Two such examples are Brakedown and its extension Orion, which enable linear-time and quantum-resistant proving by leveraging linear-time encodable Spielman codes. However, these PCS operate over large datasets, creating significant computational bottlenecks. For example, committing to and proving a degree 228 polynomial requires around 1.1 GB of data while taking 463 seconds on a high-end server CPU.This work addresses the performance bottleneck in Orion-like PCS by optimizing their most critical operations: Spielman encoding and Merkle commitments. These operations involve Gigabytes of data and suffer from random off-chip memory access patterns that drastically reduce off-chip bandwidth. We resolve this issue and introduce inverted expander graphs to eliminate random writes and reduce off-chip memory accesses by over 50%. Additionally, we propose an on-the-fly graph sampling method that avoids streaming large auxiliary data by generating expander graphs dynamically on-chip. We also provide a formal security proof for our proposed graph transformation. Beyond encoding, we accelerate Merkle Tree construction over large data sets through a scalable multi-pass SHA3 pipeline. Finally, we reutilize existing hardware components used in commitment to accelerate the so-called proximity and consistency checks during proof generation.Building upon these concepts, we present the first hardware architecture for PCS – with linear prover time – on an Xilinx Alveo U280 FPGA. In addition, we discuss the practical challenges of manually partitioning, placing, and routing our large-scale architecture to efficiently map it to the multi-SLR and HBM-equipped FPGA. The final implementation achieves a speedup of two orders of magnitude for full proof generation, covering commitment and proving steps. When combined with Virgo as an outer CP-SNARK protocol, our accelerator reduces end-to-end latency by up to 3.85x – close to the theoretical maximum of 3.9x.
We investigate the contagion effects of rapid memecoin growth, a phenomenon often characterised by irrational exuberance and illicit behaviour. Using an EGARCH methodology, the results indicate that while memecoin growth generates revenue for host platforms like Ethereum and Solana, it broadly increases market-wide risk and is detrimental to established cryptocurrencies, such as Bitcoin. Furthermore, we find that PolitiFi memecoins are uniquely susceptible, characterised by positive responses to broad memecoin growth, exhibiting statistical properties deeming them attractive due to the cloaking provided by wider memecoin market growth, without evidence for tangible purposes. • We investigate memecoin contagion effects on the cryptocurrency market using EGARCH and on-chain data. • Memecoin growth adds systemic risk towards major cryptocurrencies such as Bitcoin. • We find strong evidence of sentiment contagion from launchpads to the entire memecoin sub-class. • PolitiFi memecoins are highly susceptible to contagion, suggesting use for opaque financing. • We demonstrate that the memecoin sector is a source of systemic risk from irrationality and illicit activity.
European cybersecurity is rapidly evolving to address complex and emerging threats fueled by advancements in technology. AI-powered threat analysis has become a cornerstone, enabling faster detection of anomalies, predictive threat modeling, and real-time incident response. As Europe enters the quantum age, cybersecurity strategies are increasingly focused on quantum-resistant encryption to protect critical infrastructure and sensitive data from future quantum attacks. Simultaneously, the rise of blockchain technologies and cryptocurrencies introduces new vulnerabilities, such as smart contract exploits and decentralized finance (DeFi) fraud, requiring targeted regulatory oversight. In response, the EU is strengthening its regulatory frameworks, such as the NIS2 Directive and the Digital Operational Resilience Act (DORA), to ensure a harmonized, proactive approach to cybersecurity governance, resilience, and accountability across sectors. This multifaceted strategy reflects Europe’s commitment to safeguarding digital sovereignty and fostering trust in its digital ecosystem. The study deals with the transformation of the European cyber security ecosystem within the framework of artificial intelligence (AI) supported threat analysis. The paper discusses the security risks that arise in the quantum and post-quantum era, the possibility of blockchain/crypto systems being broken by quantum computers, the limitations of the existing data set, and the need for human-like thinking skills. In addition, the European Union's (EU) cybersecurity policies, data privacy principles, ethical standards, transparency, accountability, and human-centered AI design approaches are examined within the scope of the EU's global norm-setting role. This article also aims to shed light on the strategic steps that will shape the future of AI-powered cyber defense. Study shows that Europe should develop artificial intelligence (AI)-powered cybersecurity solutions in its preparations for the post-quantum era, it also should invest in AI models that transcend current data set limits and have humanoid thinking capacities.
Cryptocurrency services, as one of the most successful applications of blockchain technology, have recently garnered significant attention from the graph learning community. Its large-scale dynamic transaction records contain a variety of behavioral patterns and rich knowledge involving accounts, making the dynamic cryptocurrency transaction network embedding (DCTNE) a hot, yet thorny research topic. As the trading accounts increase and time accumulates, considerable transaction services are dispersed into various time slots, leading to very sparse transaction data within a time slot, that is, the transaction service data is high-dimensional and incomplete (HDI). To efficiently mine high-value knowledge from HDI data, this article proposes a proximal-ADMM-incorporated nonnegative latent-factorization-of-tensors (PNL) model for DCTNE that adopts threefold ideas: 1) incorporating the proximal terms into the alternating-direction-method-of-multipliers (ADMMs)-based learning scheme to reduce the oscillations for high estimation accuracy and fast convergence; 2) implementing a parallel training process with hyperparameter self-adaptation for high computational efficiency; and 3) proving that the proximal-incorporated learning scheme guarantees the convergence to a Karush–Kuhn–Tucker (KKT) stationary point. Experimental results on eight real-world DCTNs show that the PNL significantly outperforms several state-of-the-art (SOTA) models, demonstrating not only high efficiency and accuracy in performing DCTNE, but also strong potential to enhance the operational reliability and stability of cryptocurrency transaction systems.
Moosa Uday Kumar, P. R. Pooja, Abhishek Dixit, M. A. Jabbar
The security of smart contracts is critical to the integrity of decentralized systems. Unlike traditional software, their immutability makes them particularly susceptible to zero-day vulnerabilities unseen flaws that can lead to catastrophic financial losses once exploited. Traditional detection methods, which rely on predefined attack patterns, are fundamentally incapable of addressing such unknown threats. This paper introduces a novel deep learning framework designed to proactively detect both known and previously unobserved zero-day vulnerabilities in Ethereum smart contracts. The approach employs a dual-path architecture that combines CodeBERT for deep semantic feature extraction with two parallel detection modules: a Graph Neural Network (GNN) for classifying known threats and a dedicated Autoencoder for unsupervised anomaly detection. This dual-path system leverages the strengths of both supervised and unsupervised learning. The GNN effectively classifies known attack vectors, while the Autoencoder identifies latent anomalies by flagging contracts with high reconstruction errors, a key indicator of unseen vulnerabilities. The framework was trained and validated on a balanced subset of the Malicious Smart Contract Detection dataset. The GNN demonstrated a high classification accuracy for known vulnerabilities, and the Autoencoder successfully identified anomalous contracts that deviated from learned patterns. This dual-pronged methodology represents a significant step forward in bolstering blockchain security by providing a robust, data-driven defense against the evolving landscape of smart contract vulnerabilities.
This paper proposes a framework that integrates zero-knowledge proofs (ZKPs) and verifiable credentials (VCs) to achieve the synergistic optimization of transaction privacy and trusted identity authentication. First, a pseudonymous transaction protocol based on zk-SNARKs (zero-knowledge succinct non-interactive arguments of knowledge) is designed. The R1CS constraint system is used to construct a circuit that hides the transaction amount and address, resulting in a proof that takes up only 1.5 KB. Second, the W3C standard VC is introduced to enable off-chain identity attribute authentication. Users generate a “credential validity proof” using ZKPs and upload the proof along with the transaction to the chain. Finally, smart contracts verify the dual proofs, using Merkle tree aggregation to reduce verification overhead. An innovative “attribute-bound anonymous transaction” model is proposed to dynamically decouple transaction and identity attribute verification. Deployment on the Ethereum testnet demonstrates that this scheme reduces transaction correlation to approximately 0.3 %. Groth16 achieves a throughput of 142 transactions per second (TPS) in 798 ms at a scale of 200,000 gates.
As intelligent transportation systems (ITSs) evolve rapidly, the increasing computational demands of connected vehicles call for efficient task offloading. Centralized approaches face challenges in scalability, security, and adaptability to dynamic network conditions. To address these issues, we propose a blockchain-based decentralized task offloading framework with network-aware resource allocation and tokenized economic incentives. In our model, vehicles generate computational tasks that are dynamically mapped to available computing nodes-including vehicle-to-vehicle (V2V) resources, roadside edge servers (RSUs), and cloud data centers-based on a multi-factor score considering computational power, bandwidth, latency, and probabilistic packet loss. A blockchain transaction layer ensures auditable and secure task assignment, while a proof-of-stake (PoS) consensus and smart-contract-driven dynamic pricing jointly incentivize participation and balance workloads to minimize delay. In extensive simulations reflecting realistic ITS dynamics, our approach reduces total completion time by 12.5-24.3%, achieves a task success rate of 84.2-88.5%, improves average resource utilization to 88.9-92.7%, and sustains >480 transactions per second (TPS) with a 10 s block interval, outperforming centralized/cloud-based baselines. These results indicate that integrating blockchain incentives with network-aware offloading yields secure, scalable, and efficient management of computational resources for future ITSs.
В статье рассматриваются основы технологии нулевых доказательств знания (Zero-Knowledge Proofs, ZK), её значение для развития криптовалютных систем и децентрализованных финансов. Особое внимание уделено механизмам zk-SNARK и zk-STARK, а также их практическому применению в проектах Zcash, Ethereum, Polygon и zkSync. Проведен анализ проблем масштабируемости, вычислительной сложности и регуляторных рисков, связанных с использованием ZK-технологий. Отмечаются перспективы интеграции в архитектуру цифровых валют центральных банков и развитие инфраструктуры Web3 на базе ZK-решений
The power sector is responsible for 32 percent of global greenhouse gas emissions. Data centers and cryptocurrencies use significant amounts of electricity and contribute to these emissions. Demand-side flexibility of data centers is one possible approach for reducing greenhouse gas emissions from these industries. To explore this, we use novel data collected from the Bitcoin mining industry to investigate the impact of load flexibility on power system decarbonization. Employing engineered metrics to explore curtailment dynamics and emissions alignment, we provide the first empirical analysis of cryptocurrency data centers' capability for reducing greenhouse gas emissions in response to real-time grid signals. Our results highlight the importance of strategically aligning operational behaviors with emissions signals to maximize avoided emissions. These findings offer insights for policymakers and industry stakeholders to enhance load flexibility and meet climate goals in these otherwise energy intensive data centers.
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.
Philippe Bergault, Sébastien Bieber, Olivier Guéant, Wenkai Zhang
In traditional financial markets, yield curves are widely available for countries (and, by extension, currencies), financial institutions, and large corporates. These curves are used to calibrate stochastic interest rate models, discount future cash flows, and price financial products. Yield curves, however, can be readily computed only because of the current size and structure of bond markets. In cryptocurrency markets, where fixed-rate lending and bonds are almost nonexistent as of early 2025, the yield curve associated with each currency must be estimated by other means. In this paper, we show how mathematical tools can be used to construct yield curves for cryptocurrencies by leveraging data from the highly developed markets for cryptocurrency derivatives.
Current Ethereum fraud detection methods rely on context-independent, numerical transaction sequences, failing to capture semantic of account transactions. Furthermore, the pervasive homogeneity in Ethereum transaction records renders it challenging to learn discriminative account embeddings. Moreover, current self-supervised graph learning methods primarily learn node representations through graph reconstruction, resulting in suboptimal performance for node-level tasks like fraud account detection, while these methods also encounter scalability challenges. To tackle these challenges, we propose LMAE4Eth, a multi-view learning framework that fuses transaction semantics, masked graph embedding, and expert knowledge. We first propose a transaction-token contrastive language model (TxCLM) that transforms context-independent numerical transaction records into logically cohesive linguistic representations. To clearly characterize the semantic differences between accounts, we also use a token-aware contrastive learning pre-training objective together with the masked transaction model pre-training objective, learns high-expressive account representations. We then propose a masked account graph autoencoder (MAGAE) using generative self-supervised learning, which achieves superior node-level account detection by focusing on reconstructing account node features. To enable MAGAE to scale for large-scale training, we propose to integrate layer-neighbor sampling into the graph, which reduces the number of sampled vertices by several times without compromising training quality. Finally, using a cross-attention fusion network, we unify the embeddings of TxCLM and MAGAE to leverage the benefits of both. We evaluate our method against 21 baseline approaches on three datasets. Experimental results show that our method outperforms the best baseline by over 10% in F1-score on two of the datasets.