Blockchain Papers

Ryan Lavelle

No abstract is available for this record.

Seita Namba

No abstract is available for this record.

Mohammad Muavia

No abstract is available for this record.

Laurane Chloé Angélina Marco

We explore the design and analysis of post-quantum cryptographic primitives with an emphasis on two angles. First, diversity of assumptions, by building and analysing cryptography that does not rely on structured lattices. Second, diversity of functionalities, focusing on various primitives that extend beyond standard encryption. Motivated by the rapid development of quantum computing and the consequent threat to classical public-key cryptography, we therefore consider two families of post-quantum assumptions: isogeny-based and code-equivalence problems from which we build and analyse various primitives. In the first part, we focus on isogeny-based cryptography. We start our study with digital signatures derived from SIDH squares and investigate their security by analysing a range of attack vectors, including higher-dimensional attack strategies (known as SIDH attacks), and consequently propose suitable parameters. Building on the powerful constructive tools that the SIDH attacks became, we design an updatable public-key encryption scheme from a variant of FESTA, a public-key encryption scheme by Basso, Maino and Pope, enabling an efficient instantiation of this primitive that allows an unbounded number of updates. Finally, we investigate isogeny-based group actions and propose a framework for general-purpose zero-knowledge proofs. The second part focuses on code-based cryptography, particularly tensor group actions and code-equivalence problems. We first analyze and break a commitment scheme based on a structured tensor isomorphism problem, and we propose a secure fix. Then, we study variants of code-equivalence problems that underlie the security of two blind signature schemes. For one variant, we provide a reduction to a standard problem, whilst for another one we show that its security has been largely over-estimated. Overall, this thesis contributes to the development of a diverse suite of post-quantum primitives by providing new constructions, security analyses, and insights into the use of alternative assumptions beyond lattice-based systems.

Lian Yang, Shujiang Xu, Pingping Song, Jian Zhu · 6 authors

No abstract is available for this record.

Dinis Araujo, Ian Scott, Miguel de Castro Neto

No abstract is available for this record.

Gregory Komansky

No abstract is available for this record.

Weimin CHEN, Xiapu Luo

Decentralized finance (DeFi) is an emerging financial service on blockchain, enabling automatic and anonymous transactions.Within DeFi, decentralized exchanges (DEXs) maintain reserves of a pair of tokens and determine the exchange rate to swap tokens.However, DEXs also create opportunities for Maximal Extractable Value (MEV), where attackers include, exclude, or reorder DEX transactions to exploit price discrepancies of tokens and extract profit.Uncovering MEV opportunities requires high throughput, as the 12-second block interval and the vast search space impose strict time constraints.However, existing tools suffer from low throughput, as they rely on CPU-bound execution, which is hindered by frequent state forking and slow DEX execution.In this paper, we take the first step in leveraging GPU parallel computing power to boost MEV-search throughput in arbitrage and sandwich strategies.More precisely, we compile an MEV bot into a GPU application and then launch thousands of GPU threads to search for profit in parallel.To this end, we design new solutions to address three major challenges: designing cheatcodes to simulate transactions on GPU, proposing a memory manager to reduce GPU memory usage, and designing strategyaware mutations to improve input diversity.We implement a prototype named MeVisor that runs DEXs on GPUs and searches for MEV using a parallel genetic algorithm.Evaluated on 3,941 real MEV cases from Ethereum, MeVisor achieves 3.3M-5.1Mtransactions per second, outperforming the CPU baseline by 100,000x.In a large-scale study of Q1 2025 data, MeVisor estimates MEV opportunities ranging from 2 to 14 transactions, yielding at most $1.1 million in MEV profit.

Qihao Yuan, Zigui Jiang, Dan Li, Yuren Zhou

No abstract is available for this record.

Julius Juette

No abstract is available for this record.

Dr. Pankaj Malik, Mohit Kapoor, Akshat Gupta, Aman Singhai · 5 authors

The rapid expansion of decentralized finance (DeFi) platforms has been accompanied by a surge in rug pull scams, where malicious actors exploit liquidity pools and abandon projects, causing substantial investor losses. Existing detection approaches are largely centralized and platform-specific, limiting their effectiveness due to privacy constraints, fragmented data sources, and the dynamic behavior of blockchain ecosystems. This paper proposes a novel Federated Time-Series Learning (FTSL) framework for cross-platform rug pull detection that enables collaborative model training without sharing raw transaction data. The proposed system integrates federated learning with advanced time-series modeling to capture temporal patterns in token price volatility, liquidity changes, transaction frequency, and smart contract activities. A hybrid deep learning architecture combining Long Short-Term Memory (LSTM) networks with an attention mechanism is employed to effectively learn sequential dependencies and identify early indicators of fraudulent behavior. The federated setup ensures privacy preservation while enabling knowledge sharing across multiple decentralized platforms. Experimental results on multi-chain DeFi datasets demonstrate that the proposed FTSL model achieves 96.3% detection accuracy, outperforming traditional centralized models (91.2%) and single-platform approaches (88.7%). The model also improves precision (95.1%), recall (94.6%), and F1-score (94.8%), indicating robust and balanced performance. Furthermore, the system is capable of detecting rug pull events 6–12 hours earlier than baseline methods, providing critical early warning signals. Communication overhead is reduced by approximately 28% through optimized federated aggregation, while maintaining scalability across heterogeneous platforms. These findings highlight that Federated Time-Series Learning offers a scalable, privacy-preserving, and highly effective solution for real-time rug pull detection, contributing to enhanced security, transparency, and trust in decentralized financial ecosystems.

David Martin

No abstract is available for this record.

Anath Bandhu Chatterjee

Blockchain technology has fundamentally transformed the financial technology (fintech) landscape since Bitcoin’s introduction in 2008, evolving from peer-to-peer digital currency into comprehensive financial infrastructure. While earlier reviews catalogued blockchain applications across individual fintech verticals, rapid developments in Decentralized Finance (DeFi), Central Bank Digital Currencies (CBDCs), real-world asset (RWA) tokenization, stablecoin payment rails, and AI-blockchain convergence have created significant literature gaps. This paper presents a technically grounded review of blockchain-fintech applications as of 2025, addressing deficiencies in existing work including absent unified taxonomies, insufficient regulatory analysis, limited interoperability coverage, and inadequate treatment of institutional-grade deployments. We examine architectural underpinnings across eight application domains, integrating current market data, security analysis, and scalability benchmarks. We additionally present an analysis of the evolving threat landscape, including $3.4 billion in cryptocurrency theft during 2025. Our findings indicate the global fintech blockchain market, valued at $3.4 billion in 2024, is projected to reach $49.2 billion by 2030 at a CAGR of 55.9%, driven by institutional DeFi adoption, stablecoin settlement infrastructure, and regulatory clarity.

Dana Almajzoub, Markus Bick

No abstract is available for this record.

Shiyu Wang, Xinyu Li, Qinglin Yang, Yuan Liu

No abstract is available for this record.

Frederico C Montenegro

No abstract is available for this record.

Murillo Campello, Angela Gallo, Lira Mota, Tammaro Terracciano

No abstract is available for this record.

Serghei Smirnov

Blockchain technology and digital currencies have emerged as major disruptive forces in global finance, challenging traditional business models, financial systems, and corporate governance practices. As these technologies gain adoption, they have a profound impact on corporate financial management and, in particular, on the role of the Chief Financial Officer (CFO). The purpose of this master’s thesis is to examine how blockchain technology and cryptocurrencies influence financial management practices and to analyse the evolving responsibilities of the CFO in blockchain-oriented environments. The thesis is grounded in established theoretical frameworks on blockchain technology, distributed ledger systems, cryptocurrencies, decentralized finance (DeFi), centralized finance (CeFi), and digital financial instruments such as stablecoins, central bank digital currencies (CBDCs), initial coin offerings (ICOs), and security token offerings (STOs). These theories are complemented by literature on corporate finance, accounting standards, risk management, regulatory compliance, and technological innovation. Particular attention is given to consensus mechanisms, smart contracts, and the accounting and regulatory challenges associated with digital assets. A qualitative research approach was applied. Empirical data were collected through semi-structured interviews with CFOs and financial experts working in blockchain and fintech-related organizations. The collected data were analysed using thematic analysis to identify recurring patterns, challenges, and strategic responses related to blockchain adoption in financial management. The findings indicate that blockchain technology significantly transforms the CFO’s role by increasing the demand for technological competence, real-time financial oversight, and advanced risk management capabilities. Blockchain and cryptocurrency transactions were found to enhance transparency, improve treasury and working capital management, reduce operational costs through automation, and expand access to innovative financing methods. However, the study also identifies major challenges related to financial volatility, regulatory uncertainty, accounting treatment, and compliance obligations. The thesis concludes that while blockchain technology presents substantial strategic benefits, successful adoption requires CFOs to balance innovation with financial stability, regulatory compliance, and robust governance structures.

Frederico C Montenegro

No abstract is available for this record.

Xin Li

No abstract is available for this record.

Hojin Choi, Jaeseung Choi

Recently, extensive research has focused on addressing the unique challenges of smart contract fuzzing. Nevertheless, existing fuzzers still struggle to generate adequate function call arguments that can explore the deep smart contract states. In this paper, we introduce novel classes of argument constraints that capture the inter-argument relationships required to exercise meaningful contract logic. We propose a static analysis algorithm to extract these constraints from Solidity source code. In addition, we design a constraint-aware argument mutation strategy that leverages the identified constraints to guide test case generation for smart contract fuzzing. We implement our approach in a fuzzer named IConFuzz. Our evaluation on realistic benchmarks with integer overflow, suicidal contract, and ether leakage vulnerabilities demonstrates that IConFuzz outperforms state-of-the-art testing tools in both the number of bugs discovered and the speed of bug detection.

Erfan Moayyed, Chimay Anumba

No abstract is available for this record.

Abdur Rahman Sarker, Md. Moneruzzaman, Zikra Amin, Md Alif · 6 authors

No abstract is available for this record.

Andhika Nugraha Wira Pratama, Arya Wicaksana

Integrating artificial intelligence (AI) like the large language model (LLM) for smart contract auto-generation standardises performance and security, reduces human error, and offers accessibility for non-developers.In decentralised autonomous systems (DASs) like decentralised finance (DeFi), the ability to AI-generate smart contracts strengthens the decentralisation and automation characteristics of the applications.In order to increase the effectiveness of a smart contract's fully decentralised and autonomous development, this study benchmarks gas-saving patterns in AI-generated DeFi smart contracts.Three DeFI smart contract development scenarios: token generation (ERC-20), tokenised vault (ERC-4626), and flash loan (ERC-3156), and the state-of-the-art LLMs (Code Llama and Code Llama -Python) are explored to study the gas-saving patterns of AI-generated smart contracts.These results help optimise DeFi smart contracts created by AI regarding gas fees for the same operations.