We analyze developer activity across 10 major Ethereum repositories (totaling 129884 commits, 40550 issues) spanning 10 years to examine how events such as technical upgrades, market events, and community decisions impact development. Through statistical, survival, and network analyses, we find that technical events prompt increased activity before the event, followed by reduced commit rates afterwards, whereas market events lead to more reactive development. Core infrastructure repositories like Go-Ethereum exhibit faster issue resolution compared to developer tools, and technical events enhance core team collaboration. Our findings show how different types of events shape development dynamics, offering insights for project managers and developers in maintaining development momentum through major transitions. This work contributes to understanding the resilience of development communities and their adaptation to ecosystem changes.
Mathias Hall-Andersen, Mark Simkin, Benedikt Wagner
Towards building more scalable blockchains, an approach known as data availability sampling (DAS) has emerged over the past few years. Even large blockchains like Ethereum are planning to eventually deploy DAS to improve their scalability. In a nutshell, DAS allows the participants of a network to ensure the full availability of some data without any one participant downloading it entirely. Despite the significant practical interest that DAS has received, there are currently no formal definitions for this primitive, no security notions, and no security proofs for any candidate constructions. For a cryptographic primitive that may end up being widely deployed in large real-world systems, this is a rather unsatisfactory state of affairs. In this work, we initiate a cryptographic study of data availability sampling. To this end, we define data availability sampling precisely as a clean cryptographic primitive. Then, we show how data availability sampling relates to erasure codes. We do so by defining a new type of commitment schemes which naturally generalizes vector commitments and polynomial commitments. Using our framework, we analyze existing constructions and prove them secure. In addition, we give new constructions which are based on weaker assumptions, computationally more efficient, and do not rely on a trusted setup, at the cost of slightly larger communication complexity. Finally, we evaluate the trade-offs of the different constructions.
Rashid Ul Haq, Rahim Khan, Fahad Alturise, Shafrida Sahrani · 6 authors
Recent technological advances have enabled researchers to investigate various novel approaches utilized to manage allograft transplants and overcome the challenges of conventional centralized systems. The rising need for transparency, efficiency, and, especially, security in this highly sensitive medical procedure necessitates the use of decentralized solutions like blockchain rather than existing centralized approaches. However, the current state of research is theoretical and unproven, and allograft management lacks any reliable, cost-effective, or data-proven solution. In this paper, we propose an Ethereum blockchain-based allograft transplantation management system that can address all of those issues linked to the existing solutions. The proposed approach aims to enhance traceability, transparency, and data provenance across the entire allograft transplant process. We present six reliable and cost-efficient algorithms, as well as a comprehensive system architecture, to provide valuable insight into system implementation complexity. We have designed an efficient smart contract implementing the proposed algorithms to ensure flawless execution of allograft donation, transportation, and transplantation. We conduct thorough tests, validation, security, cost, throughput, and latency assessments of the system in order to contrast its effectiveness with existing solutions and results shows that our solution is cost-effective, as well as secure and efficient. We generalized the proposed solution so that, with minimal changes, it could be used for other problems and addressed some of the technical and ethical challenges.
Omar Isaac Asensio, Catherine E. Moore, Nícola Ulibarrí, Mecit Can Emre Simsekler · 6 authors
Abstract Data for Policy ( dataforpolicy.org ), a trans-disciplinary community of research and practice, has emerged around the application and evaluation of data technologies and analytics for policy and governance. Research in this area has involved cross-sector collaborations, but the areas of emphasis have previously been unclear. Within the Data for Policy framework of six focus areas, this report offers a landscape review of Focus Area 2: Technologies and Analytics. Taking stock of recent advancements and challenges can help shape research priorities for this community. We highlight four commonly used technologies for prediction and inference that leverage datasets from the digital environment: machine learning (ML) and artificial intelligence systems, the internet-of-things, digital twins, and distributed ledger systems. We review innovations in research evaluation and discuss future directions for policy decision-making.
Modern enterprises generate vast volumes of data across distributed applications, cloud platforms, and digital services. Traditional centralized data governance models struggle to scale in such complex environments, leading to data silos, inconsistent governance enforcement, and limited data accessibility. Autonomous data platforms supported by artificial intelligence (AI) offer a promising solution by integrating self-service infrastructure, automated governance mechanisms, and intelligent metadata management. AI-driven governance frameworks can automate tasks such as data discovery, classification, lineage tracking, anomaly detection, and compliance monitoring. This article explores the architectural foundations of autonomous data platforms and examines how AI-driven governance enables scalable, decentralized, and trustworthy data ecosystems. Drawing on emerging concepts such as data mesh architectures, federated governance models, and responsible AI frameworks, the paper proposes a conceptual model for building intelligent and self-governing enterprise data platforms. In such environments, machine learning algorithms continuously analyze data flows, schema evolution, usage patterns, and policy compliance to dynamically enforce governance rules and improve data quality. Metadata-driven architectures further enable automated cataloging, semantic enrichment, and real-time lineage tracking, allowing organizations to maintain transparency and accountability across complex data pipelines. By embedding governance directly into the data infrastructure, autonomous platforms reduce operational overhead while empowering domain teams to manage their own data products within standardized governance policies. Furthermore, the integration of explainable AI techniques and policy-aware automation ensures that governance decisions remain auditable, fair, and aligned with regulatory requirements. Ultimately, the convergence of AI, distributed data architectures, and intelligent metadata management provides a scalable foundation for building resilient, adaptive, and trustworthy enterprise data ecosystems capable of supporting advanced analytics, machine learning, and data-driven decision-making.
Amir M. Ebrahimi, Bram Adams, Gustavo A. Oliva, Ahmed E. Hassan
Software applications that run on a blockchain platform are known as DApps. DApps are built using smart contracts, which are immutable after deployment. Just like any real-world software system, DApps need to receive new features and bug fixes over time in order to remain useful and secure. However, Ethereum lacks native solutions for post-deployment smart contract maintenance, requiring developers to devise their own methods. A popular method is known as the upgradeability proxy contract (UPC), which involves implementing the proxy design pattern (as defined by the Gang of Four). In this method, client calls first hit a proxy contract, which then delegates calls to a certain implementation contract. Most importantly, the proxy contract can be reconfigured during runtime to delegate calls to another implementation contract, effectively enabling application upgrades. For researchers, the accurate detection of UPCs is a strong requirement in the understanding of how exactly real-world DApps are maintained over time. For practitioners, the accurate detection of UPCs is crucial for providing application behavior transparency and enabling auditing. In this paper, we introduce UPC Sentinel, a novel three-layer algorithm that utilizes both static and dynamic analysis of smart contract bytecode to accurately detect active UPCs. We evaluated UPC Sentinel using two distinct ground truth datasets. In the first dataset, our method demonstrated a near-perfect accuracy of 99%. The evaluation on the second dataset further established our method's efficacy, showing a perfect precision rate of 100% and a near-perfect recall of 99.3%, outperforming the state of the art. Finally, we discuss the potential value of UPC Sentinel in advancing future research efforts.
Consensus algorithms play a critical role in maintaining the consistency of blockchain data, directly affecting the system's security and stability, and are used to determine the binary consensus of whether proposals are correct. With the development of blockchain-related technologies, social choice issues such as Bitcoin scaling and main chain forks, as well as the proliferation of decentralized autonomous organization (DAO) applications based on blockchain technology, require consensus algorithms to reach consensus on a specific proposal among multiple proposals based on node preferences, thereby addressing the multi-value consensus problem. However, existing consensus algorithms, including Practical Byzantine Fault Tolerance (PBFT), do not support nodes expressing preferences. Instead, the proposal to reach consensus is directly decided by specific nodes, with other nodes merely verifying the proposal's validity, which can easily result in monopolistic or dictatorial outcomes. In response, we proposed the Aggregating Preferences with Practical Byzantine Fault Tolerance (AP-PBFT) consensus algorithm, which allows nodes to express preferences for multiple proposals. AP-PBFT ensures the validity of consensus results through a consensus output protocol, and incentivizes nodes to act honestly during the consensus process by incentive mechanism. First, AP-PBFT leverages Verifiable Random Function to select both consensus nodes and a primary node from the candidates. The primary node gathers proposals, assembles them into a proposal package, and broadcasts it to other consensus nodes. The consensus nodes independently vote to express their preferences for different proposals in the package, execute the consensus output protocol to reach local consensus, and the primary node aggregates these results to form the global consensus. Once the global consensus is finalized, AP-PBFT evaluates node behavior based on the consensus output protocol, penalizes nodes that acted maliciously, and rewards those that adhered to the protocol. Additionally, nodes can interact and adopt different strategies while executing the consensus output protocol, which can influence the consensus outcome. Therefore, we established an evolutionary game model based on hypergraph to analyze these interactions. Theoretical analysis shows that the incentive mechanism in AP-PBFT effectively encourages nodes to honestly follow the consensus output protocol, ensuring that AP-PBFT satisfies the properties of consistency, validity, and termination. Finally, the simulation results demonstrate that the AP-PBFT algorithm possesses good scalability and the capability to handle dynamic changes in nodes, surpassing some mainstream consensus algorithms in terms of transaction throughput and consensus achievement time. Moreover, AP-PBFT can incentivize honest behavior among consensus nodes, thereby enhancing the reliability of consensus and strengthening the security of the network.
Provable Data Possession (PDP) is an alternative technique that guarantees the integrity of remote data. However, most current PDP schemes are inapplicable to similarity-like data checking with the same attribute, i.e., when there are numerous similar files to be checked by Data Owners (DOs). Some traditional models cannot resist the corrupt auditors who always generate biased challenge information. Besides, a copy-summation attack exists in some schemes, which means the Cloud Server (CS) can bypass the verification by storing the median value instead of initial data via summation operation. To address the issues above, in this work, we propose a keyword searchable PDP scheme for large similar data checking. To achieve searchability, we introduce the notion of a keyword in PDP and design a specific index structure to match the authenticator. The scheme enables all matched files to be auditable and verifiable, while guaranteeing privacy protections. Unlike existing methods, our Third Party Auditor (TPA) checks all similar data containing the same keyword simultaneously. We utilize unpredictable yet verifiable public information on the blockchain to generate challenge information, rather than relying on a centralized TPA. The proposed scheme can resist copy-summation attacks. Theoretical analysis demonstrates that the proposed scheme satisfies the security requirements, and our evaluations demonstrate its efficiency.
Lock-in practices of online services hinder consumers from switching frictionlessly to a competitor once they are unsatisfied with the company’s service offering, privacy practices, or philosophy. The right to data portability (RtDP) is one of the strongest measures introduced by recent privacy regulations to unlock continuously collected user data from centralized silos of market leaders. Introducing the obligation to provide means of data transfers between services, it aims to establish decentralized online markets and to foster competition. In this longitudinal study comprising a unique dataset of 129 online services over three consecutive years, we are the first to provide evidence on the development of the effectiveness of the EU’s RtDP. Astonishingly, only 16% of services could provide a compliant data export in all years, with services from the industries Entertainment and Travel performing worst. Overall, Finance & Insurance and Social Networks & Messaging include the services with the highest compliance rates. Regarding the usefulness of data portability, our analysis unveils that data export scope and data import options have stagnated between 2020 and 2022. Further, we are able to show that online services with a high presence of third-party trackers are less compliant and ready to export data from their systems. Lastly, our regression analyses show that service popularity significantly increases format compliance, export scope, and import options. This suggests that competitors to incumbents still perceive the regulation more as a bureaucratic burden than a unique opportunity to attract new consumers and their data.
The burgeoning size and complexity of Large Language Models (LLMs) introduce significant challenges in ensuring data integrity. The proliferation of "deep fakes" and manipulated information raises concerns about the vulnerability of LLMs to misinformation. Traditional LLM architectures often lack robust mechanisms for tracking the origin and history of training data. This opaqueness can leave LLMs susceptible to manipulation by malicious actors who inject biased or inaccurate data. This research proposes a novel approach integrating Blockchain Technology (BCT) within the LLM data supply chain. With its core principle of a distributed and immutable ledger, BCT offers a compelling solution to address this challenge. By storing the LLM's data supply chain on a blockchain, we establish a verifiable record of data provenance. This allows for tracing the origin of each data point used to train the LLM, fostering greater transparency and trust in the model's outputs. This decentralised approach minimises the risk of single points of failure and manipulation. Additionally, the immutability of blockchain records ensures that the data provenance remains tamper-proof, further enhancing the trustworthiness of the LLM. Our approach leverages three critical features of BCT to strengthen LLM security: 1) Transaction Anonymity: While data provenance is recorded on the blockchain, identities of data contributors can be anonymised, protecting their privacy while ensuring data integrity. 2) Decentralised Repository: Enhances the system's resilience against potential attacks by distributing the data provenance record across the blockchain network. 3) Block Validation: Rigorous consensus mechanisms ensure the validity of each data point added to the LLM's data supply chain - minimising the risk of incorporating inaccurate or manipulated data into the training process. Using the experimental approach, initial evaluations using simulated LLM training data on a blockchain platform demonstrate the feasibility and effectiveness of the proposed approach in enhancing data integrity. This approach has far-reaching implications for ensuring the trustworthiness of LLMs in various applications.
The scaled Web 3.0 digital economy, represented by decentralized finance (DeFi), has sparked increasing interest in the past few years, which usually relies on blockchain for token transfer and diverse transaction logic. However, illegal behaviors, such as financial fraud, hacker attacks, and money laundering, are rampant in the blockchain ecosystem and seriously threaten its integrity and security. In this paper, we propose a novel double graph-based Ethereum account de-anonymization inference method, dubbed DBG4ETH, which aims to capture the behavioral patterns of accounts comprehensively and has more robust analytical and judgment capabilities for current complex and continuously generated transaction behaviors. Specifically, we first construct a global static graph to build complex interactions between the various account nodes for all transaction data. Then, we also construct a local dynamic graph to learn about the gradual evolution of transactions over different periods. Different graphs focus on information from different perspectives, and features of global and local, static and dynamic transaction graphs are available through DBG4ETH. In addition, we propose an adaptive confidence calibration method to predict the results by feeding the calibrated weighted prediction values into the classifier. Experimental results show that DBG4ETH achieves state-of-the-art results in the account identification task, improving the F1-score by at least 3.75% and up to 40.52% compared to processing each graph type individually and outperforming similar account identity inference methods by 5.23 % to 12.91 %.
The group signature with designated traceability (GSdT) is a kind of group signatures (GS) which aim to restrict the opening authority of the group manager; by setting an access structure over openers’ attributes at the signing, a signer is able to control openers who can open the signature. A generic construction of GSdT was given when the notion was introduced, then a pairing-based construction and a symmetric-key-based one were presented. Nonetheless, it remains open whether or not a post-quantum GSdT that has full anonymity can be really constructed.In this paper, we give a lattice-based GSdT scheme that has full anonymity for the first time. In our construction, the lattice-based ciphertext-policy attribute-based encryption (CP-ABE) by Tsabary and the lattice-based group signatures (GS) by Libert et al. are employed. The CP-ABE is based on the Regev public-key encryption, while the GS uses a non-interactive zero-knowledge proof to prove the correctness of the encryption in the signing process. Based on the compatibility, we combine and modify them to build up a GSdT scheme.
Our EHR Management System (EMS) empowers patients to control their electronic health records (EHR), en-hancing data privacy and access control. The system allows patients to carry their data as modular units on cost-effective, resource-constrained devices like Raspberry Pi, Beaglebone, and ESP32. We implemented EMS in two scenarios: one device per patient and one device shared among multiple patients. Using Blockchain-based Non-Fungible Tokens (NFTs), our system ensures secure access and authentication for authorized users. We evaluated our EMS by measuring delays in accessing data and verifying NFTs in a hospital scenario where two types of patients are scheduled to general and specialized doctors hourly. Despite using low-cost devices, scheduling delays were minimal. Among the tested scheduling techniques-Modified Queue-based, Reinforcement Learning (RL), and Deep Reinforcement Learning (Deep RL)-the Modified Queue-based method showed the least delay, proving efficient for our EMS.
Jan Lauinger, Jens Ernstberger, Andreas Finkenzeller, Sebastian Steinhorst
Web users can gather data from secure endpoints and demonstrate the provenance of sensitive data to any third party by using privacy-preserving TLS oracles. In practice, privacy-preserving TLS oracles remain limited and cannot verify larger, sensitive data sets. In this work, we introduce new optimizations for TLS oracles, which enhance the efficiency of selectively verifying the provenance of confidential web data. The novelty of our work is a construction which secures an honest verifier zero-knowledge proof system in the asymmetric privacy setting while retaining security against malicious adversaries. Concerning TLS 1.3 in the one round-trip time (1-RTT) mode, we propose a new, optimized garble-then-prove paradigm in a security setting with malicious adversaries. Our improvements reach new performance benchmarks and facilitate a practical deployment of privacy-preserving TLS oracles in web browsers.
The rapid increase in the number of electric vehicles (EVs) has resulted in huge fuel tax losses for governments every year. Many countries have levied taxes based on the annual or monthly travel record (TR) submitted by the EV. On the one hand, TR contains important private information, such as the time, locations, and trajectories of EV owners. On the other hand, EV owners may forge TR to reduce taxes. Therefore, the verification protocol of TR requires extremely high security and effectiveness. To solve this outstanding issue, this paper proposes a V2I-SNARK protocol that combines vehicle-to-infrastructure communications (V2I) and zk-SNARK for TR verification of EVs. V2I -SNARK is divided into two stages, the trusted setup stage and the TR verification stage. In the former stage, a trusted authority (TA) will generate the proof key and verification key for verification and store them on the verification server (Verifier). In the latter stage, EV will use the proof key to generate a randomized proof, and the verifier will use the verification key to verify the proof. Regarding the performance of the V2I -SNARK protocol, we first provide security proofs for completeness, soundness, and zero-knowledge properties. Furthermore, we compare the verification efficiency, energy consumption, computational complexity, and other performance of V2I-SNARK with the benchmark protocols. The results show that the proposed V2I-SNARK protocol outperforms other protocols in terms of verification efficiency and energy consumption.
Pohsun Feng, Ziqian Bi, Yan, Lawrence K. Q., Yizhu Wen · 17 authors
A detailed exploration of blockchain technology and its applications across various fields is provided, beginning with an introduction to cryptography fundamentals, including symmetric and asymmetric encryption, and their roles in ensuring security and trust within blockchain systems. The structure and mechanics of Bitcoin and Ethereum are then examined, covering topics such as proof-of-work, proof-of-stake, and smart contracts. Practical applications of blockchain in industries like decentralized finance (DeFi), supply chain management, and identity authentication are highlighted. The discussion also extends to consensus mechanisms and scalability challenges in blockchain, offering insights into emerging technologies like Layer 2 solutions and cross-chain interoperability. The current state of academic research on blockchain and its potential future developments are also addressed.
The increasing complexity of data management systems, coupled with the evolving nature of cybersecurity threats, necessitates innovative approaches to ensure data integrity, confidentiality, and availability. This paper explores recent studies on advanced data management strategies and their intersection with cybersecurity practices. Key insights are drawn from the latest research on topics such as distributed ledger technologies, artificial intelligence-driven threat detection, and privacy-preserving data management frameworks. The analysis highlights how these emerging technologies are reshaping the landscape of data management while addressing cybersecurity challenges. Additionally, this paper examines the role of regulation and policy in fostering secure data ecosystems. The findings offer a comprehensive overview of current trends, challenges, and opportunities in the field, with recommendations for future research directions.
Jean Gilbert Mbula Mboma, Obed Tshimanga Tshipata, Witesyavwirwa Vianney Kambale, Mohamed Salem · 6 authors
Verifying the authenticity of documents, whether digital or physical, is a complex and crucial challenge faced by a variety of entities, including governments, regulators, financial institutions, educational establishments, and healthcare services. Rapid advances in technology have facilitated the creation of falsified or fraudulent documents, calling into question the credibility and authenticity of academic records. Most existing blockchain-based verification methods and systems focus primarily on verifying the integrity of a document, paying less attention to examining the authenticity of the document’s actual content before it is validated and registered in the system, thus opening loopholes for clever forgeries or falsifications. This paper details the design and implementation of a proof-of-concept system that combines GPT-3.5’s natural language processing prowess with the Ethereum blockchain and the InterPlanetary File System (IPFS) for storing and verifying documents. It explains how a Large Language Model like GPT-3.5 extracts essential information from academic documents and encrypts it before storing it in the blockchain ensuring document integrity and authenticity. The system is tested for its efficiency in handling both digital and physical documents, demonstrating increased security and reliability in academic document verification.
Tarik Chafiq, Rida Azmi, Abdelhamid Fadil, Mohammed Ouadoud
Blockchain technology holds transformative potential for geospatial data sharing by offering enhanced security, transparency, and decentralization. This paper explores the integration of blockchain into managing geospatial data, focusing on its capabilities to revolutionize data storage, identification, property rights confirmation, and traceability. Through a comprehensive review of current blockchain applications and a detailed analysis of scalability, privacy, security, and cost concerns, this study identifies key challenges hindering blockchain adoption in geospatial data workflows. To address these issues, the research proposes solutions such as off-chain scaling, advanced encryption techniques, and cloud-based blockchain infrastructures. The study emphasizes the importance of aligning blockchain applications with existing legal and regulatory frameworks, including GDPR and other data protection laws. While blockchain presents significant opportunities for improving geospatial data management, this study concludes that successful implementation requires overcoming technical and regulatory hurdles. Future research should focus on developing standardized protocols and exploring innovative use cases to maximize the benefits of blockchain in geospatial contexts. • Blockchain offers potential solution for geospatial data sharing. • Identifies and discusses the significant applications of blockchain for sharing geospatial data. • Blockchain can play a decisive role in handling deception in the field of geospatial data. • Blockchain technology offers secure & transparent geospatial data sharing. • Identify and ensure the integrity and reliability of the data.
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.
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.