Blockchain Papers

Tharindu Ranathunga, Sourabh Bharti, Alan McGibney

No abstract is available for this record.

Pranav Yede, Rohit Vichare, Aarya Shelar, Priyanka Shingane

No abstract is available for this record.

Shlomi Dolev, Ehud Gudes, Daniel Shlomo

No abstract is available for this record.

Glenda Carla Moura Amaral

No abstract is available for this record.

Yunfei Zhao

No abstract is available for this record.

Krishnaveni Katta

In this paper, I explore various machine-learning models for predicting high-frequency returns for four of the most popular cryptocurrency perpetual futures trading pairs: BTCUSDT, ETHUSDT, MATICUSDT, and SOLUSDT. Specifically, I train and evaluate models for classifying the direction of the smoothed mid-price change for high-frequency prediction horizons. I introduce a novel data set constructed from a live WebSocket stream from Binance, the world's largest centralized cryptocurrency exchange. I explore how different data representations affect model performance and how performance varies for different trading pairs and prediction horizons. I use a mixture of traditional machine learning and deep learning models and show that simple and explainable traditional models can rival the performance of far larger and more complex state-of-the-art deep learning models.

Niranjan Sapkota

This research explores the factors contributing to the failure of cryptocurrency exchanges by analyzing a sample of 845 exchanges. Using logit and probit models, it identifies key variables affecting cryptocurrency exchange defaults. The results show that cryptocurrency exchanges that are centralized, located in countries with high transparency indices, and offer fewer peer cryptocurrencies are more likely to default. Additionally, exchanges that impose high withdrawal fees and have no restrictions on clients from the United States are also positively associated with defaults. Moreover, the absence of referral schemes and having lower ratings each contributes marginally to defaults. Machine learning (ML) models including random forest, support vector machine, stacked ensemble confirm the robustness and high predictability of cryptocurrency exchange defaults. • This study uses statistical and ML models to predict cryptocurrency exchange default. • Centralized exchanges from high-transparency-index nations are more prone to default. • Limited coin listings, high fees, and U.S. client access increase the default risk. • Lacking referral programs and low ratings both marginally contribute to default.

Guillermo Arroyo Jiménez, J Laguna Garcia

The aim of this article is to examine the reasons why cryptocurrency volatility hinders its potential to replace fiat money as legal tender. We focus on Bitcoin and Ethereum for this analysis. By applying an augmented Dickey-Fuller stationarity test, we demonstrate that cryptocurrencies lack a long-term trend; instead, their movement is erratic and highly volatile. Furthermore, eGARCH models indicate that volatility tends to decrease and is expected to persist in this pattern. In summary, theoretical and empirical analysis suggests that, due to their nature based solely on supply and demand and their high volatility, cryptocurrencies are not suitable as primary investment instruments or stores of value.

Trevor Conley, Nady Carolina Díaz, Diego Espada, Alvin Kuruvilla · 6 authors

No abstract is available for this record.

Vincent Capol, Yuxi Liu, Haibo Xiu, Jun Yang

Modern database systems are expected to handle dynamic data whose characteristics may evolve over time. Many popular database benchmarks are limited in their ability to evaluate this dynamic aspect of the database systems. Those that use synthetic data generators often fail to capture the complexity and unpredictable nature of real data, while most real-world datasets are static and difficult to create high-volume, realistic updates for. This paper introduces CrypQ, a database benchmark leveraging dynamic, public Ethereum blockchain data. CrypQ offers a high-volume, ever-evolving dataset reflecting the unpredictable nature of a real and active cryptocurrency market. We detail CrypQ's schema, procedures for creating data snapshots and update sequences, and a suite of relevant SQL queries. As an example, we demonstrate CrypQ's utility in evaluating cost-based query optimizers on complex, evolving data distributions with real-world skewness and dependencies.

Lewis Tseng, Lokesh Ambarapu, Moayad Aloqaily

Distributed Hash Tables (DHT) is used for achieving robust routing and overlay for most Blockchain systems. In other words, DHT supports communication in a wide spectrum of Web3 and Blockchain applications, such as data sharing, content distribution, and distributed search engines. The decentralized nature of DHT introduces security and privacy vulnerabilities. Significant efforts have been devoted to making DHT more robust and augmenting DHT’s privacy guarantees. This paper proposes a novel design, namely NC-DHT, which has two main features: (i) NC-DHT tolerates Byzantine nodes, and (ii) NC-DHT provides initiator anonymity, query unlinkability and target privacy. To the best of our knowledge, NC-DHT is the first DHT that supports all these important features, which makes it suitable as an overlay for Blockchains. NC-DHT relies on a novel integration of quorum topology and network coding-based techniques.

Stefano Tempesta

Cryptography plays a vital role in ensuring the security and privacy of blockchain networks, as well as Web3 dApps and wallets. This chapter describes some common cryptographic concepts and algorithms relevant to Web3, which include: Hash Functions : Hash functions transform data into fixed-length strings (hashes). In Web3, hashes are used to verify data integrity and create unique identifiers for transactions and blocks. Public and Private Key Pairs : Web3 relies on asymmetric encryption, where each user has a public key (used for encryption) and a corresponding private key (used for decryption). Public keys are openly shared, while private keys must remain confidential. Transactions and data integrity are secured using these key pairs. Symmetric Encryption : Symmetric encryption uses a single shared secret key for both encryption and decryption. While less common in Web3, it&s;s still relevant for certain use cases. Digital Signatures : Digital signatures provide proof of authenticity and integrity. When a user signs a transaction with their private key, others can verify it using the associated public key. Zero-Knowledge Proofs : Zero-Knowledge Proofs are cryptographic protocols that allow one party (the prover) to prove the truth of a statement to another party (the verifier) without revealing any additional information beyond the statement&s;s validity. In other words, ZKPs enable verification without disclosure.

Zaryab Khan, Muhammad Hasan Ali Baig, Muhammad Waqas, Syeda Mariam Muzammal · 6 authors

Blockchain technology has groundbreaking success, especially in the Non-Fungible Token (NFT) space where artists, creators, and collectors alike are getting amazing opportunities. This hype around NFTs is real, and getting countless fans who are fascinated by their power to fully define digital assets via blockchain technology. It covers a vast range of digital assets, NFTs contain items such as digital collectibles, artwork, and even virtual land. A decentralized application (DApp) is used for NFT minting which provides a reliable and easy to use system for users to design their NFTs and list them. This paper presents NFT minting DApp, which is designed for the creation and management of NFTs on the blockchain. The aim is to streamline the NFT generation process, offering users an intuitive platform to effortlessly upload diverse digital assets like images, videos, audio files, and compatible formats. With the user-friendly DApp guiding through every step of the minting process, users can easily define metadata and customize features associated with the unique NFTs. To accomplish this, the NFT minting DApp utilizes blockchain technology, with a focus on the Ethereum network. Ethereum stands as the ideal basis for the proposed NFT minting DApp, delivering a robust and widely embraced blockchain platform that completely supports smart contracts. It aims to empower individuals by equipping them with an accessible platform to mint their distinctive digital assets, with a primary focus on digital art. It also enables users to actively engage in the rapidly evolving NFT ecosystem, which is reshaping the way digital creations are perceived and exchanged.

Yashasvi Sorapalli, Meeth Jitendra Davda, Aaryan Guglani, Akshat Gada · 7 authors

This research introduces a solution to escalating data security and privacy concerns by utilizing Ethereum-based blockchain technologies, like Polygon, and NFTs for decentralized document storage and authentication. Unique NFTs associated with each document are cryptographic proofs, minted and recorded on the blockchain to ensure resilience and tamper resistance. This research experiments with the use of Solidity based smart contract along with a decentralized file storage protocal, IPFS, to enhance flexibility and scalability.

Ka Wai Wu

The rise of Decentralized Finance (DeFi) has brought novel financial opportunities but also exposed serious security vulnerabilities, with flash loans frequently exploited for price manipulation attacks. These attacks, leveraging the atomic nature of flash loans, allow malicious actors to manipulate DeFi protocol oracles and pricing mechanisms within a single transaction, causing substantial financial losses. Traditional smart contract analysis tools address some security risks but often struggle to detect the complex, inter-contract dependencies that make flash loan attacks challenging to identify. In response, we introduce FlashDeFier, an advanced detection framework that enhances static taint analysis to target price manipulation vulnerabilities arising from flash loans. FlashDeFier expands the scope of taint sources and sinks, enabling comprehensive analysis of data flows across DeFi protocols. The framework constructs detailed inter-contract call graphs to capture sophisticated data flow patterns, significantly improving detection accuracy. Tested against a dataset of high-profile DeFi incidents, FlashDeFier identifies 76.4% of price manipulation vulnerabilities, marking a 30% improvement over DeFiTainter. These results highlight the importance of adaptive detection frameworks that evolve alongside DeFi threats, underscoring the need for hybrid approaches combining static, dynamic, and symbolic analysis methods for resilient DeFi security.

Francesco D’Amato, Roberto Saltini, Thuy-An Tran, Luca Zanolini

Gasper, the consensus protocol currently employed by Ethereum, typically requires 64 to 95 slots -- the units of time during which a new chain extending the previous one by one block is proposed and voted -- to finalize. This means that under ideal conditions -- where the network is synchronous, and all chain proposers, along with more than two-thirds of the validators, behave as dictated by the protocol -- proposers construct blocks on a non-finalized chain that extends at least 64 blocks. This exposes a significant portion of the blockchain to potential reorganizations during changes in network conditions, such as periods of asynchrony. Specifically, this finalization delay heightens the network's exposure to Maximum Extractable Value (MEV) exploits, which could undermine the network's integrity. Furthermore, the extended finalization period forces users to balance the trade-off between economic security and transaction speed. To address these issues and speed up finality, we introduce a partially synchronous finality gadget, which we combine with two dynamically available consensus protocols -- synchronous protocols that ensure safety and liveness even with fluctuating validator participation levels. This integration results in secure ebb-and-flow protocols [SP 2021], achieving finality within three slots after a proposal and realizing 3-slot finality.

Marek Kirejczyk, Maciej Kalka, Leonid Logvinov

This paper presents a comprehensive analysis of storage proofs in the Ethereum ecosystem, examining their role in addressing historical and cross-chain state access challenges. We systematically review existing approaches to historical state verification, comparing Merkle Mountain Range (MMR) and Merkle-Patricia trie (MPT) architectures. An analysis involves their respective performance characteristics within zero-knowledge contexts, where performance challenges related to Keccak-256 are explored. The paper also examines the cross-chain verification, particularly focusing on the interactions between Ethereum and Layer 2 networks. Through careful analysis of storage proof patterns across different network configurations, we identify and formalize three architectures for cross-chain verification. By organizing this complex technical landscape, this analysis provides a structured framework for understanding storage proof implementations in the Ethereum ecosystem, offering insights into their practical applications and limitations.

Lehao Lin, Hong Kang, Xinyao Sun, Wei Cai

Non-Fungible Tokens (NFTs) have emerged as a pivotal digital asset, offering authenticated ownership of unique digital content. Despite it has gained remarkable traction, yet face pressing storage and verification challenges stemming from blockchain's permanent data costs. Existing off-chain or centralized storage solutions, while being alternatives, also introduce notable security vulnerabilities. We present SemNFT, an innovative decentralized framework integrated with blockchain oracle middleware services, addressing these persistent NFT dilemmas. Our approach compresses NFT source data into compact embeddings encapsulating semantic essence. These arrays are stored on-chain, while facilitating reliable decentralized image reconstruction and ownership verification. We implemented ERC721-compliant smart contracts with supplementary functionalities, demonstrating SemNFT's seamless integrative capabilities within the ecosystem. Extensive evaluations evidence marked storage optimizations and preservation of requisite visual fidelity by comparison with existing solutions. The proposed SemNFT framework marks a significant advancement in holistically confronting rising NFT storage and verification challenges without compromising decentralization. It substantively propels the meaningful evolution of NFT infrastructure to achieve digital asset immortality.

Mariam Elkhechafi, Jihane Aayale

Portfolio optimization is a key area in financial engineering, aiming to maximize investor returns. In this study, we propose an innovative portfolio management system based on Deep Reinforcement learning. Unlike traditional approaches, our system incorporates five parameters, including trading volume, for a more comprehensive market analysis. Furthermore, we leverage multiple data sources simultaneously, providing a more robust perspective. Our approach also stands out by utilizing specially designed deep convolution to separately address each parameter. Additionally, we introduce a five-dimensional attention gating network to better identify critical moments and high-potential assets. Simulation results demonstrate that our approach significantly outperforms traditional methods, offering investors more substantial returns and reduced short-term risk.

Bayu Al Ikhlas Swari, I Made Suartana

Penelitian ini mengembangkan aplikasi terdesentralisasi (DApp) untuk manajemen sertifikat digital menggunakan Non-Fungible Token (NFT) berbasis standar ERC-721 di jaringan Ethereum. Aplikasi ini dirancang untuk meningkatkan keamanan, dan transparansi dalam pembuatan, pengelolaan, serta verifikasi sertifikat digital. Hasil penelitian menunjukan bahwa penggunaan teknologi blockchain memberikan jaminan bahwa setiap sertifikat yang diterbitkan tidak hanya dapat dilacak dan diverifikasi secara publik, tetapi juga tidak dapat diubah atau dimanipulasi setelah diterbitkan. Hal ini membuat sistem lebih aman dibandingkan dengan metode tradisional. Implementasi standar ERC-721 memungkinkan setiap sertifikat digital yang diterbitkan dalam bentuk NFT memiliki identitas unik yang tidak dapat diduplikasi atau dipalsukan. Selain itu, teknologi ini juga memungkinkan pemilik sertifikat untuk memiliki bukti kepemilikan yang aman dan dapat diverifikasi oleh pihak ketiga. Pengembangan dan pengujian smart contract yang ditulis dalam bahasa pemrograman Solidity pada platform Ethereum juga berhasil membuktikan bahwa smart contract yang dibuat berfungsi dengan baik dalam berbagai aspek seperti penciptaan, transfer, verifikasi, dan penghapusan sertifikat. Smart contract ini menunjukkan keamanan sesuai dengan spesifikasi yang diharapkan, menjadikannya solusi inovatif untuk manajemen sertifikat digital. Kata Kunci - Blockchain, Ethereum, DApp, Sertifikat Digital, NFT, ERC-721.

Junyuan Liang, Wuhui Chen, Zicong Hong, Haogang Zhu · 6 authors

Blockchain store states in Log-Structured Merge (LSM) tree-based database. Due to blockchain traceability, the growing ancient states are inevitably stored in the databases. Unfortunately, by default, this process mixescurrentandancientstates in the data layout, increasing unnecessary disk I/O access and slowing transaction execution. This paper proposes MoltDB, a scalable LSM-based database for efficient transaction execution through a novel idea ofancient state segregation, i.e., to segregate current and ancient states in the data layout. However, the frequently generated and uncertainly accessed characteristics of ancient states make the segregation challenging. Thus, we develop an “extract-compact” mechanism to batch extraction process for frequently generated ancient states and the LSM compaction process to relieve additional disk I/O overhead. Moreover, we design an adaptive LSM-based storage for the uncertainly accessed ancient states extracted for on-demand access. We implement MoltDB as a database engine compatible with many mainstream blockchains and integrate it into Ethereum for evaluation. Experimental results show that MoltDB achieves 1.3 × transaction throughput and 30% disk I/O latency savings over the state-of-the-art works.

Nima Shiri Harzevili, Mohammad Mahdi Mohajer, Jiho Shin, Moshi Wei · 11 authors

Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These bugs are often concealed in the input validation and error-checking code of DL libraries and can lead to silent failures, incorrect results, or unexpected program behavior in DL applications. Despite their potential to significantly impact the reliability and performance of DL-enabled systems built with these libraries, checker bugs have received limited attention. We present the first comprehensive study of DL checker bugs in two widely-used DL libraries, i.e., TensorFlow and PyTorch. Initially, we automatically collected a dataset of 2,418 commits from TensorFlow and PyTorch repositories on GitHub from Sept. 2016 to Dec. 2023 using specific keywords related to checker bugs. Through manual inspection, we identified 527 DL checker bugs. Subsequently, we analyzed these bugs from three perspectives, i.e., root causes, symptoms, and fixing patterns. Using the knowledge gained via root cause analysis of checker bugs, we further propose TensorGuard, a proof-of-concept RAG-based LLM-based tool to detect and fix checker bugs in DL libraries via prompt engineering a series of ChatGPT prompts. We evaluated TensorGuard's performance on a test dataset that includes 92 buggy and 135 clean checker-related changes in TensorFlow and PyTorch from January 2024 to July 2024. Our results demonstrate that TensorGuard has high average recall (94.51\%) using Chain of Thought prompting, a balanced performance between precision and recall using Zero-Shot prompting and Few-Shot prompting strategies. In terms of patch generation, TensorGuard achieves an accuracy of 11.1\%, which outperforms the state-of-the-art bug repair baseline by 2\%. We have also applied TensorGuard on the latest six months' checker-related changes (493 changes) of the JAX library from Google, which resulted in the detection of 64 new checker bugs.

Viktor Valaštín, Dušan Morháč, Kristián Košťál, Ivan Kotuliak

Liquidity is critical for a healthy and thriving blockchain ecosystem, enabling value exchange between participants. However, achieving unified liquidity across heterogeneous blockchain platforms remains challenging due to disparities in architecture, virtual machines, and asset management logic. These disparities force assets to be wrapped into other formats to ensure compatibility with underlying systems, thus fragmenting liquidity into multiple pools. This paper proposes LiquiSpell, a novel protocol that aims to unify liquidity across multiple parachains within the Polkadot ecosystem. By leveraging the cross-chain message passing (XCMP), LiquiSpell introduces the concept of a universal transaction that can be constructed to be compatible with any parachain, regardless of its underlying architecture or asset management pallet. This approach overcomes the obstacles posed by the diverse nature of parachains, enabling seamless asset sharing and enhancing cross-chain interoperability. The proposed solution mitigates liquidity fragmentation within the Polkadot ecosystem. It presents a framework that can be extended to other multichain environments outside Polkadot. Ultimately, LiquiSpell aims to foster a thriving ecosystem by facilitating the introduction of new assets and increasing overall liquidity, thereby driving innovation and adoption within the decentralized finance (DeFi) landscape.

Hari Kishore Chaparala, Sai Vineeth Doddala, Ahmad Showail, Faisal Nawab

Integrating non-fungible tokens (NFTs) into decentralized user-facing applications like virtual worlds presents significant performance and security challenges. Traditional NFT marketplaces struggle with scalability and security, particularly when off-chain solutions are employed to enhance performance. To tackle these issues, we introduce DecentEdge (Decentralized Edge), a trusted off-chain NFT Marketplace (NFTM) designed to operate closer to the user at the edge. DecentEdge stands out by enabling concurrent, secure processing of multiple transactions on the same NFT off-chain, significantly boosting scalability. This is achieved through two key design ingredients: leveraging Trusted Execution Environments (TEEs) to secure off-chain operations, and implementing a Multi-Stage NFT transaction processing protocol. This protocol splits NFT transactions into off-chain and on-chain stages, effectively reducing the impact of on-chain latency in typical execution paths. However, this approach introduces potential conflicts between off-chain and on-chain stages, which we address with a carefully designed conflict resolution mechanism to ensure safety and security. Our experiments-conducted on a blockchain network-show that DecentEdge achieves over 300-fold throughput improvement when compared to the state-of-the-art NFTM, OpenSea.