Ovaj diplomski rad istražuje volatilnost kriptovaluta kroz analizu ključnih financijskih rizika. Na početku rada pruža se detaljan pregled blockchain tehnologije i povijesti kriptovaluta, te se raspravlja o njihovim karakteristikama i razlikama u odnosu na tradicionalne metode plaćanja. Također u radu se daje pregled sigurnosnih rizika povezanih s kriptovalutama i izazovima koje donosi njihova regulacija i analizira trenutne trendove, te se istražuju mogući scenariji za budućnost kriptovaluta. U analizi volatilnosti kriptovaluta korišteni su ključni pokazatelji financijskog rizika kao što su standardna devijacija, varijanca, korelacija i beta koeficijent. Rad uspoređuje volatilnost odabranih kriptovaluta, uključujući Bitcoin, Ethereum, Binance Coin, XRP i Litecoin, koji se uspoređuju s indeksom S&P 500. Rezultati pokazuju da, iako kriptovalute nude potencijalno visoke prinose, one također nose značajan rizik zbog svoje izrazite volatilnosti. Ova analiza pruža dublje razumijevanje rizika povezanih s ulaganjem u kriptovalute i ističe važnost pažljivog razmatranja tih rizika prilikom donošenja investicijskih odluka. Iako su kriptovalute inovativan i privlačan oblik imovine, njihova nestabilnost u usporedbi s tradicionalnim financijskim instrumentima zahtijeva oprez i temeljitu analizu prije ulaganja.
Decompilation of binary code has arisen as a highly-important application in the space of Ethereum VM (EVM) smart contracts. Major new decompilers appear nearly every year and attain popularity, for a multitude of reverse-engineering or tool-building purposes. Technically, the problem is fundamental: it consists of recovering high-level control flow from a highly-optimized continuation-passing-style (CPS) representation. Architecturally, decompilers can be built using either static analysis or symbolic execution techniques. We present Shrnkr, a static-analysis-based decompiler succeeding the state-of-the-art Elipmoc decompiler. Shrnkr manages to achieve drastic improvements relative to the state of the art, in all significant dimensions: scalability, completeness, precision. Chief among the techniques employed is a new variant of static analysis context: shrinking context sensitivity. Shrinking context sensitivity performs deep cuts in the static analysis context, eagerly “forgetting” control-flow history, in order to leave room for further precise reasoning. We compare Shrnkr to state-of-the-art decompilers, both static-analysis- and symbolic-execution-based. In a standard benchmark set, Shrnkr scales to over 99.5% of contracts (compared to ∼95% for Elipmoc), covers (i.e., reaches and manages to decompile) 67% more code than Heimdall-rs, and reduces key imprecision metrics by over 65%, compared again to Elipmoc.
Proto-Danksharding, proposed in Ethereum Improvement Proposal 4844 (EIP-4844), aims to incrementally improve the scalability of the Ethereum blockchain by introducing a new type of transaction known as blob-carrying transactions. These transactions incorporate binary large objects (blobs) of data that are stored off-chain but referenced and verified on-chain to ensure data availability. By decoupling data availability from transaction execution, Proto-Danksharding alleviates network congestion and reduces gas fees, laying the groundwork for future, more advanced sharding solutions. This letter provides an analytical model to derive the delay for these new transactions. We model the system as an $\mathrm{M/D}^B/1$ queue which we then find its steady state distribution through embedding a Markov chain and use of supplementary variable method. We show that transactions with more blobs but less frequent impose higher delays on the system compared to lower blobs but more frequent.
We analyze the token transfer network on Ethereum, focusing on accounts associated with Alameda Research, a cryptocurrency trading firm implicated in the misuse of FTX customer funds. Using a multi-token network representation, we examine node centralities and the network backbone to identify critical accounts, tokens, and activity groups. The temporal evolution of Alameda accounts reveals shifts in token accumulation and distribution patterns leading up to its bankruptcy in November 2022. Through network analysis, our work offers insights into the activities and dynamics that shape the DeFi ecosystem.
This paper explores the volatility spillover effects between the cryptocurrency market and the Pakistan Stock Exchange (PSX). Utilising data from January 1, 2019, to April 5, 2024, sourced from Investing and Yahoo Finance, the study employs the Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) BEKK model to assess the dynamic interactions between these markets. Stationarity tests confirmed the non-stationarity of time series data at their levels, which became stationary after first differencing, ensuring robust econometric analysis. The results indicate significant volatility spillovers from major cryptocurrencies, such as Bitcoin and Ethereum, to the PSX, highlighting a solid interconnectedness between these markets. This suggests that digital asset volatility significantly influences traditional financial systems. The study concludes that integrating cryptocurrencies into global financial markets introduces risks and opportunities for investors and policymakers. The findings underscore the need for market participants to account for these volatility interactions in their risk management strategies. Additionally, policymakers must consider these interlinkages to maintain financial stability. This research contributes to the literature on financial market volatility by emphasising the importance of understanding the impact of emerging digital currencies on traditional stock markets.
Павло Чуб, Олена Олександрівна Примостка, Антон Пащенко
Метою статті є аналіз правових аспектів регулювання криптовалют в провідних країнах світу (США та ЄС), дослідження можливостей і переваг інтеграції криптовалюти та блокчейну в банківську систему, а також виявлення потенційних ризиків та викликів, пов'язаних з цим процесом. Розглянуто технічні та правові аспекти (на прикладі США та ЄС) інтеграції криптовалют та блокчейну в банківську систему. Особливу увагу приділено Bitcoin (BTC) та Ethereum (ETH) як найпоширенішим криптовалютам. Аналіз технічних аспектів показав, що ці криптовалюти функціонують на базі децентралізованої технології блокчейну, яка забезпечує безпеку, прозорість та незмінність транзакцій. Проаналізуємо регулювання ринку криптовалют в ЄС. Проаналізовано потенційні ризики та виклики, пов'язані з інтеграцією цифрових активів, такі як висока волатильність криптовалют, проблеми з легалізацією та регулюванням цифрових активів та ключові напрями консалтингу. Серед проблем регулювання криптовалют слід виділити насамперед відсутність правил для спотових ринків та ризики регуляторного арбітражу. Доведено, що інтеграція криптовалюти та блокчейну в банківську систему може бути вигідною для банків та їх клієнтів, проте все ще потребує ретельного вивчення технологічних та правових аспектів задля запобігання насамперед можливим зловживанням та кримінальній діяльності. Проаналізовано ризики, пов'язані з інтеграцією криптовалют та блокчейну в банківську систему.
The large-scale deployment of Solidity smart contracts on the Ethereum mainnet has increasingly attracted financially-motivated attackers in recent years. A few now-infamous attacks in Ethereum's history includes DAO attack in 2016 (50 million dollars lost), Parity Wallet hack in 2017 (146 million dollars locked), Beautychain's token BEC in 2018 (900 million dollars market value fell to 0), and NFT gaming blockchain breach in 2022 ($600 million in Ether stolen). This paper presents a comprehensive investigation of the use of large language models (LLMs) and their capabilities in detecting OWASP Top Ten vulnerabilities in Solidity. We introduce a novel, class-balanced, structured, and labeled dataset named VulSmart, which we use to benchmark and compare the performance of open-source LLMs such as CodeLlama, Llama2, CodeT5 and Falcon, alongside closed-source models like GPT-3.5 Turbo and GPT-4o Mini. Our proposed SmartVD framework is rigorously tested against these models through extensive automated and manual evaluations, utilizing BLEU and ROUGE metrics to assess the effectiveness of vulnerability detection in smart contracts. We also explore three distinct prompting strategies-zero-shot, few-shot, and chain-of-thought-to evaluate the multi-class classification and generative capabilities of the SmartVD framework. Our findings reveal that SmartVD outperforms its open-source counterparts and even exceeds the performance of closed-source base models like GPT-3.5 and GPT-4 Mini. After fine-tuning, the closed-source models, GPT-3.5 Turbo and GPT-4o Mini, achieved remarkable performance with 99% accuracy in detecting vulnerabilities, 94% in identifying their types, and 98% in determining severity. Notably, SmartVD performs best with the `chain-of-thought' prompting technique, whereas the fine-tuned closed-source models excel with the `zero-shot' prompting approach.
Noor Ul Ain Tahir, Umer Rashid, Hassan Jalil Hadi, Naveed Ahmad · 7 authors
This study investigated the potential of blockchain technology to transform Electronic Health Record (EHR) administration, integrity, and security. EHRs store vital health information such as medical history, diagnosis, prescriptions, and imaging findings, which may be shared with healthcare professionals to improve patient care. The existing EHR systems have a centralized framework. These centralized systems have a single point of failure, data management, integrity, and security concerns. Blockchain technology provides a solution to these problems by delivering benefits such as safety, privacy, secrecy, and decentralization. This study presents a framework for adopting blockchain technology in EHR systems, providing a comprehensive, modular, and straightforward approach. Our proposed framework addresses the constraints of existing EHR systems by providing a platform for connected and interoperable EHRs. The proposed blockchain-based patient health records management framework demonstrates the potential to address the limitations of current centralized health records systems. It offers benefits such as data privacy and security, interoperability, audibility, decentralization, and automation through the use of smart contracts. The proposed framework is implemented in Ethereum. The evaluation, i.e., cost and performance results, show that this solution is reasonable and may be used on any blockchain network, whether it is permissioned or permissionless.
Traditional volatility models do not work well when volatility changes rapidly and in the presence of outliers. Therefore, two lines of improvements have been developed separately in the existing literature. Range-based models benefit from efficient volatility estimates based on low and high prices, while robust methods deal with outliers. We propose a range-based GARCH model with a bounded M-estimator, which combines these two improvements with a third new improvement: a modified robust method, which adds elasticity in treating the outliers. We apply this model to Bitcoin , Ethereum Classic, Ethereum, and Litecoin and find that it forecasts variances, value at risk, and expected shortfall more accurately than the standard GARCH model, the standard range-based GARCH model, and the GARCH model with the robust estimation. Utilization of high and low prices joined with a novel treatment of outliers makes our model perform well during extreme periods when traditional volatility models fail.
Juhani Latvakoski, Jouni Heikkinen, Jari Palosaari, Vesa Kyllönen · 5 authors
The aim of this research has been to enable the management of trustworthy relationships between stakeholders, service providers, and physical assets, which are required in critical energy and mobility cyber–physical systems (CPS) applications. The achieved novel contribution is the concept of trustworthy communities with respective experimental solutions, which are developed by relying on verifiable credentials, smart contracts, trust over IP, and an Ethereum-based distributed ledger. The provided trustworthy community solutions are validated by executing them in two practical use cases, which are called energy flexibility and hunting safety. The energy flexibility case validation considered the execution of the solutions with one simulated and two real buildings with the energy flexibility aggregation platform, which was able to trade the flexibilities in an energy flexibility marketplace. The provided solutions were executed with a hunting safety smartphone application for a hunter and the smartwatch of a person moving around in the forest. The evaluations indicate that conceptual solutions for trustworthy communities fulfill the purpose and contribute toward making energy flexibility trading and hunting safety possible and trustworthy enough for participants. A trustworthy community solution is required to make value sharing and usage of critical energy resources and their flexibilities feasible and secure enough for their owners as part of the energy flexibility community. Sharing the presence and location in mobile conditions requires a trustworthy community solution because of security and privacy reasons, but it can also save lives in real-life elk hunting cases. During the evaluations, the need for further studies related to performance, scalability, community applications, verifiable credentials with wallets, sharing of values and incentives, authorized trust networks, dynamic trust situations, time-sensitive behavior, autonomous operations with smart contracts through security assessment, and applicability have been detected.
R. Shashidhara, Renju Chirakarotu Nair, Pavan Kumar Panakalapati
ABSTRACT Blockchain technology has revolutionized data sharing and storage, yet maintaining privacy and security while ensuring transparency and immutability remains a significant challenge. This research addresses this challenge by investigating the promising solution of Zero‐Knowledge Proofs (ZKPs), which allow for validation without revealing additional information. Unlike previous studies that focus on specific ZKP types or limited performance metrics, this article provides a comprehensive analysis of multiple ZKP technologies, along with their distinct features and blockchain applications. We evaluate ZKP tools such as snarkjs, ZoKrates, and Circom, focusing on key factors including proof size, trusted setup, prover time, verifier time, and scalability. A detailed case study on the Ethereum blockchain demonstrates the practical implementation and performance of ZKPs, effectively bridging the gap between theoretical analysis and real‐world applications. Furthermore, we explore how ZKPs can significantly enhance scalability in blockchain applications. By proposing innovative ZKP use cases and addressing current challenges, this article not only provides a comprehensive understanding of ZKP technologies but also outlines potential future research directions, making a significant impact on the field of blockchain privacy and security.
Konrad Weiss, Christof Ferreira Torres, Florian Wendland
Ethereum smart contracts are executable programs deployed on a blockchain. Once deployed, they cannot be updated due to their inherent immutability. Moreover, they often manage valuable assets that are worth millions of dollars, making them attractive targets for attackers. The introduction of vulnerabilities in programs due to the reuse of vulnerable code posted on Q&A websites such as Stack Overflow is not a new issue. However, little effort has been made to analyze the extent of this issue on deployed smart contracts. In this paper, we conduct a study on the impact of vulnerable code reuse from Q&A websites during the development of smart contracts and provide tools uniquely fit to detect vulnerable code patterns in complete and incomplete Smart Contract code. This paper proposes a pattern-based vulnerability detection tool that is able to analyze code snippets (i.e., incomplete code) as well as full smart contracts based on the concept of code property graphs. We also propose a methodology that leverages fuzzy hashing to quickly detect code clones of vulnerable snippets among deployed smart contracts. Our results show that our vulnerability search, as well as our code clone detection, are comparable to state-of-the-art while being applicable to code snippets. Our large-scale study on 18,660 code snippets reveals that 4,596 of them are vulnerable, out of which 616 can be found in 7,852 deployed smart contracts. These results highlight that the reuse of vulnerable code snippets is indeed an issue in currently deployed smart contracts.
Security is an important consideration when delivering information-aware messages to vehicles that are far away from the current location of the information-sending vehicle. This information helps the receiver to save fuel and time by making wise decisions to avoid damaged or blocked roads. To ensure the safety and security of this type of information using blockchain technology, we propose a new cognitive vehicular communication scheme to transfer messages from source to destination. Due to spectrum scarcity in vehicular networks, there needs to be a wireless medium available for every communication link since vehicles require it to communicate. The primary user (PU) makes a public announcement about a free channel to all secondary users nearby and only gives it to authentic vehicles. The authenticity of vehicles is guaranteed by a roadside unit (RSU) that offers secure keys to any vehicle that joins this blockchain network. Those who participate in this network must pay a certain amount and receive rewards for their honesty that exceed the amount spent. To test the performance of various parameters, the proposed scheme utilizes the Ethereum smart contract and compares them to blockchain and non-blockchain methods. Our results show a minimum delivery time of 0.16 s and a minimum overhead of 350 bytes in such a dynamic vehicle environment.
Samuel de Oliveira Ribeiro, Saul Sousa da Rocha, Dayan Ramos Gomes, Nara Raquel D. Andrade · 6 authors
The support for smart contracts on the Ethereum blockchain network has enabled the emergence of a new digital market centered around Non-Fungible Tokens (NFTs), unique digital assets that have transformed the way value is assigned and exchanged in the decentralized web.This chapter provides a detailed exploration of three essential aspects that drive the NFT ecosystem: (1) the technical and operational fundamentals of creating and commercializing NFTs, (2) the categorization and automated analysis of collections using the OpenSea API, and (3) the challenges and future opportunities related to scalability, price prediction, and the impact of communities on the NFT market.We present the principles underpinning NFTs, highlighting OpenSea as the largest trading platform for these assets.Subsequently, we discuss data collection and automated categorization techniques, which are crucial for organizing and analyzing the market.Finally, we explore recent research and developments that will address challenges and enhance the growth of the NFT market in the coming years. ResumoO suporte a contratos inteligentes na rede blockchain Ethereum viabilizou o surgimento de um novo mercado digital baseado em Tokens No Fungveis (NFTs), ativos digitais nicos que transformaram a forma como o valor atribudo e trocado na web descentralizada.Este captulo aborda de forma detalhada trs aspectos essenciais que impulsionam o ecossistema dos NFTs: (1) os fundamentos tcnicos e operacionais da criao e comercializao de NFTs, (2) a categorizao e anlise automatizada de colees utilizando a API da OpenSea, e (3) os desafios e oportunidades futuras relacionados escalabilidade, predio de preos e o impacto das comunidades no mercado de NFTs.Apresentamos os
Thiago Dias Monteiro, Otavio Próspero Sanchez, Gustavo Hermínio Salati Marcondes de Moraes
Purpose The current scenario shows increased adoption of off-chain voting applications in decentralized autonomous organizations (DAOs) to avoid complexity and costs for user engagement in their governance. This paper aims to comprehend the trends and patterns within the DAO community with off-chain voting systems and blockchain usage. Design/methodology/approach This exploratory study uses a two-pronged quantitative approach, combining descriptive statistical analysis and fuzzy-set qualitative comparative analysis. Input data from different DAO proposals and votes were analyzed. Findings Results indicate that the 179 DAOs analyzed account for 26,538 proposals and 1,268,474 followers of the entire Snapshot base in January 2023, showing high concentration in engagement. The preferred social media communication channels are Twitter and GitHub, showing a decrease of Ethereum as a main chain, with Polygon and Binance Smart Chain already accounting for 22%. Basic voting can lead to greater user involvement in the voting process. Research limitations/implications This study complements the traditional governance theory perspective and advances in the emerging literature on DAOs and blockchain with an empirical analysis. Practical implications As blockchain technology gains increasing adoption, comprehending trends and patterns within the DAO community becomes pivotal in identifying opportunities and challenges for innovation and improvement. Social implications Findings can facilitate the development of improved governance models and decision-making processes for DAOs, leading to a more robust and sustainable ecosystem. Originality/value This study presents a broader IT-based governance foresight via blockchain while providing an understanding of the voting process, technology features and governance mechanisms for social community engagement and decision-making in DAOs.
Gilbert Fridgen, Roman Kräussl, Orestis Papageorgiou, Alessandro Tugnetti
Abstract This paper analyzes the sales of 875,389 art nonfungible tokens (NFTs) on the Ethereum blockchain to identify the key determinants influencing NFT pricing and market dynamics. We find that market liquidity and trade volume are strong predictors of NFT prices. Contrarily, social media activity negatively correlates with prices. Introducing an artist ranking system, our study reveals a “superstar effect”, with a few artists dominating sales, and herding behaviour within the NFT market.
Ethereum faces growing fraud threats. Current fraud detection methods, whether employing graph neural networks or sequence models, fail to consider the semantic information and similarity patterns within transactions. Moreover, these approaches do not leverage the potential synergistic benefits of combining both types of models. To address these challenges, we propose TLMG4Eth that combines a transaction language model with graph-based methods to capture semantic, similarity, and structural features of transaction data in Ethereum. We first propose a transaction language model that converts numerical transaction data into meaningful transaction sentences, enabling the model to learn explicit transaction semantics. Then, we propose a transaction attribute similarity graph to learn transaction similarity information, enabling us to capture intuitive insights into transaction anomalies. Additionally, we construct an account interaction graph to capture the structural information of the account transaction network. We employ a deep multi-head attention network to fuse transaction semantic and similarity embeddings, and ultimately propose a joint training approach for the multi-head attention network and the account interaction graph to obtain the synergistic benefits of both.
Procurement processes within Enterprise Resource Planning (ERP) systems are essential for managing supplier relationships, purchase orders, and payment settlements. Conventional ERP-based procurement often faces challenges related to inefficiency, manual reconciliation, lack of transparency, and susceptibility to fraud or contractual disputes. Blockchain-enabled smart contracts offer a transformative approach by providing self-executing agreements that operate on decentralized, tamper-resistant ledgers. These contracts ensure automation, trust, and compliance, thereby addressing long-standing limitations in procurement workflows. This paper investigates the integration of blockchain technology with ERP-based procurement systems, focusing on how smart contracts can automate vendor onboarding, streamline purchase order execution, enforce payment terms, and ensure regulatory compliance. The discussion emphasizes architectural principles for blockchain-ERP integration, including transparency, immutability, interoperability, and governance. Various blockchain platforms such as Hyperledger Fabric, Ethereum, and Corda are assessed for their suitability in enterprise procurement environments, considering factors such as permissioned access, scalability, and regulatory alignment. Through reference models and case studies, the paper demonstrates how blockchain enabled procurement enhances efficiency, reduces disputes, and strengthens supply chain accountability. Key challenges including interoperability with legacy ERP, smart contract vulnerabilities, and compliance with global regulations are also explored. The study provides a conceptual and practical framework for designing blockchain-enabled procurement architectures, highlighting their potential to reshape ERP ecosystems and support sustainable digital transformation.
UAVs are increasingly being used in various domains, from personal and commercial applications to military operations. Ensuring the security and trustworthiness of UAV communications is crucial, and blockchain technology has been explored as a solution. However, privacy remains a challenge, especially in public blockchains. In this work, we propose a novel approach utilizing zero-knowledge proof techniques, specifically zk-SNARKs, which are non-interactive cryptographic proofs. This approach allows UAVs to prove their authenticity or location without disclosing sensitive information. We generated zk-SNARK proofs using the Zokrates tool on a Raspberry Pi, simulating a drone environment, and analyzed power consumption and CPU utilization. The results are promising, especially in the case of larger drones with higher battery capacities. Ethereum was chosen as the public blockchain platform, with smart contracts developed in Solidity and tested on the Sepolia testnet using Remix IDE. This novel proposed approach paves the way for a new path of research in the UAV area.
The advent of cryptocurrencies has revolutionized the financial landscape, offering a new and innovative way for individuals to invest and manage their wealth. Investing in cryptocurrencies, such as Bitcoin, Ethereum, and others, has numerous advantages that make it an attractive option for those seeking to diversify their portfolios. One of the primary benefits of investing in cryptocurrencies is their potential for high returns. As the popularity and adoption of cryptocurrencies continue to grow, their value can fluctuate significantly, providing investors with substantial profits. Additionally, cryptocurrencies offer a level of liquidity and accessibility that is unmatched by traditional assets, allowing investors to buy and sell quickly and easily. This article aims to explore the advantages of investing in cryptocurrencies and their perspective in the future. We will examine the benefits of diversifying investment portfolios with cryptocurrencies, the potential for high returns, and the potential risks involved. Furthermore, we will discuss the current state of the market and the future prospects of cryptocurrencies.
This study explores the balance between data privacy and regulatory compliance in blockchain-based financial systems, focusing on privacy-enhancing technologies (PETs) such as Zero-Knowledge Proofs (ZKPs) and multiparty computations (MPCs). Through a comprehensive methodology combining literature review, comparative analysis, and empirical testing on the Ethereum test network, the research reveals significant trade-offs. Implementing ZKPs increased transaction times from 5 seconds to 12 seconds and gas fees from 0.02 ETH to 0.05 ETH, while computational load rose by 60%, highlighting the impact on scalability and efficiency. Chi-Square tests and regression analysis uncovered notable algorithmic biases, with low-value accounts experiencing 15% fewer transaction approvals and small mining pools receiving 20% fewer rewards than larger counterparts. Additionally, MPCs, while offering robust privacy, increased communication overhead by 35%, posing scalability challenges. The study recommends adopting a tiered privacy approach, implementing basic privacy measures for low-sensitivity transactions, and advanced technologies like ZKPs for high-sensitivity transactions while optimizing ZKPs to reduce their computational burden and enhance transaction speeds, and integrating artificial intelligence to detect and mitigate algorithmic biases in blockchain systems. Future research should also explore hybrid privacy solutions that combine the strengths of different PETs, such as ZKPs and MPCs, to achieve both robust privacy and high efficiency. Furthermore, investigating quantum-resistant cryptographic methods is crucial to safeguarding blockchain systems against emerging threats. These insights provide valuable guidance for financial institutions, blockchain developers, and policymakers, promoting the development of blockchain-based financial systems that optimize data privacy while maintaining system performance and regulatory compliance.
Shivani Jamwal, José Cano, Gyu Myoung Lee, Nguyen H. Tran · 5 authors
Blockchain technology has emerged as a transformative force in various sectors, including finance, healthcare, supply chains, and intellectual property management. Beyond Bitcoin’s role as a decentralized payment system, Ethereum represents a notable application of blockchain, featuring Smart Contract functionality that enables the development and execution of decentralized applications (DApps). A key feature of Ethereum , and public blockchains in general, is pseudonymity, typically achieved by using public keys as pseudonyms for users. Despite implementing several privacy-preserving techniques, the public recording of user activities on the blockchain allows various deanonymization methods that can profile users, reveal sensitive information , and potentially re-identify them. Most blockchains, such as Bitcoin , Litecoin , and Cardano, employ the Unspent Transaction Output (UTXO) model for accounting, which focuses on individual transactions and is susceptible to various deanonymization techniques. In contrast, Ethereum uses an account-based transaction model, integrating the concepts of accounts and wallets at the protocol level. This makes most UTXO-based deanonymization techniques ineffective for Ethereum. However, alternative methods with the potential to deanonymize Ethereum users have been proposed and developed. Privacy preservation techniques have been used to counteract deanonymization attempts; however, the challenges related to these techniques, their effectiveness and efficiency, and the trade-off between usability and protection levels remain areas for further exploration. This survey presents a comprehensive analysis of state-of-the-art privacy preservation along with deanonymization techniques in the blockchain and Ethereum ecosystems. This survey examines the intrinsic mechanisms supporting pseudonymity in Ethereum, providing a detailed assessment of the advantages and disadvantages of privacy preservation techniques, and suggests potential countermeasures against those deanonymization methods. It also discusses the implications arising from the intersection of DApps and data protection legislation , which is vital for ensuring the coexistence and advancement of groundbreaking blockchain capabilities and protecting user data.
The imbalance between parking availability and demand has led to a rise in traffic challenges in many cities. The adoption of technologies like the Internet of Things and deep learning algorithms has been extensively explored to build automated smart parking systems in urban environments. Non-human-mediated, scalable smart parking systems that are built on decentralized blockchain systems will further enhance transparency and trust in this domain. The presented work, PARKTag, is an integration of a blockchain-based system and computer vision models to detect on-field free parking slots, efficiently navigate vehicles to those slots, and automate the computation of parking fees. This innovative approach aims to enhance the efficiency, scalability, and convenience of parking management by leveraging and integrating advanced technologies for real-time slot detection, navigation, and secure, transparent fee calculation with blockchain smart contracts. PARKTag was evaluated through implementation and emulation in selected areas of the MIT Art Design Technology University campus, with a customized built-in dataset of over 2000 images collected on-field in different conditions. The fine-tuned parking slot detection model leverages pre-trained algorithms and achieves significant performance metrics with a validation accuracy of 92.9% in free slot detection. With the Solidity smart contract deployed on the Ethereum test network, PARKTag achieved a significant throughput of 10 user requests per second in peak traffic hours. PARKTag is implemented as a mobile application and deployed in the mobile application store. Its beta version has undergone user validation for feedback and acceptance, marking a significant step toward the development of the final product.
Many blockchain networks aim to preserve the anonymity of validators in the peer-to-peer (P2P) network, ensuring that no adversary can link a validator's identifier to the IP address of a peer due to associated privacy and security concerns. This work demonstrates that the Ethereum P2P network does not offer this anonymity. We present a methodology that enables any node in the network to identify validators hosted on connected peers and empirically verify the feasibility of our proposed method. Using data collected from four nodes over three days, we locate more than 15% of Ethereum validators in the P2P network. The insights gained from our deanonymization technique provide valuable information on the distribution of validators across peers, their geographic locations, and hosting organizations. We further discuss the implications and risks associated with the lack of anonymity in the P2P network and propose methods to help validators protect their privacy. The Ethereum Foundation has awarded us a bug bounty, acknowledging the impact of our results.