It is stated that the Decentralized Finance (DeFi) is transforming the financial industry because it provides its users with such services as lending, borrowing, trading, and insurance on decentralized terms using blockchain technology. DeFi is cheaper, transparent, and secure because it is developed on decentralized platforms, including Ethereum. But it is also encircled by such threats as regulatory risk, security risk and market risk. In this paper, the comparison of the DeFi and traditional financial systems will be presented involving such central capabilities of the DeFi as decentralized exchanges, liquidity pools, and lending platforms. It also mentions the discrepancies in rules, the positives of financial inclusions and the DeFi negatives such as volatility and absence of consumer protections. The paper gives a concluding look at what traditional institutions and regulators can do to react and cooperate with DeFi.
Pasquale De Rosa, Simon Queyrut, Yérom-David Bromberg, Pascal Felber · 5 authors
The Ethereum Virtual Machine (EVM) is a decentralized computing engine. It enables the Ethereum blockchain to execute smart contracts and decentralized applications (dApps). The increasing adoption of Ethereum sparked the rise of phishing activities. Phishing attacks often target users through deceptive means, e.g., fake websites, wallet scams, or malicious smart contracts, aiming to steal sensitive information or funds. A timely detection of phishing activities in the EVM is therefore crucial to preserve the user trust and network integrity. Some state-of-the art approaches to phishing detection in smart contracts rely on the online analysis of transactions and their traces. However, replaying transactions often exposes sensitive user data and interactions, with several security concerns. In this work, we present PhishingHook, a framework that applies machine learning techniques to detect phishing activities in smart contracts by directly analyzing the contract’s bytecode and its constituent opcodes. We evaluate the efficacy of such techniques in identifying malicious patterns, suspicious function calls, or anomalous behaviors within the contract’s code itself before it is deployed or interacted with. We experimentally compare 16 techniques, belonging to four main categories (Histogram Similarity Classifiers, Vision Models, Language Models and Vulnerability Detection Models), using 7,000 real-world malware smart contracts. Our results demonstrate the efficiency of PhishingHook in performing phishing classification systems, with about 90% average accuracy among all the models. We support experimental reproducibility, and we release our code and datasets to the research community.
Initially introduced to Ethereum via Flashbots' MEV-boost, Proposer-Builder Separation allows proposers to auction off blockspace to a market of transaction orderers, known as builders. PBS is currently available to validators through the aforementioned MEV-boost, but its unregulated and relay-dependent nature has much of the Ethereum community calling for its enshrinement. Providing a protocol-integrated PBS marketspace and communication channel for payload outsourcing is termed PBS enshrinement. Although ePBS potentially introduces native MEV mitigation mechanisms and reduces validator operation costs, fears of multiparty collusion and chain stagnation are all too real. In addition to mitigating these potential drawbacks, PBS research pursues many tenets revered by Web3 enthusiasts, including but not limited to, censorship resistance, validator reward equity, and deflationary finance. The subsequent SoK will identify current PBS mechanisms, the need for enshrinement, additions to the ePBS upgrade, and the existing or potential on-chain socioeconomic implications of each.
Emerging crypto economies still hemorrhage digital assets because legacy wallets leak private keys at almost every layer of the software stack, from user-space libraries to kernel memory dumps. This paper solves that twin crisis of security and interoperability by re-imagining key management as a platform-level service anchored in ARM TrustZone through OP-TEE. Our architecture fractures the traditional monolithic Trusted Application into per-chain modules housed in a multi-tenant TA store, finally breaking OP-TEE's single-binary ceiling. A cryptographically sealed firmware-over-the-air pipeline welds each TA set to an Android system image, enabling hot-swap updates while Verified Boot enforces rollback protection. Every package carries a chained signature developer first, registry second so even a compromised supply chain cannot smuggle malicious code past the Secure World's RSA-PSS gatekeeper. Inside the TEE, strict inter-TA isolation, cache partitioning, and GP-compliant crypto APIs ensure secrets never bleed across trust boundaries or timing domains. The Rich Execution Environment can interact only via hardware-mediated Secure Monitor Calls, collapsing the surface exposed to malware in Android space. End-users enjoy a single polished interface yet can install or retire Bitcoin, Ethereum, Solana, or tomorrow's chain with one tap, shrinking both storage footprint and audit scope. For auditors, the composition model slashes duplicated verification effort by quarantining blockchain logic inside narrowly scoped modules that share formally specified interfaces. Our threat analysis spans six adversary layers and shows how the design neutralizes REE malware sniffing, OTA injection, and cross-module side channels without exotic hardware. A reference implementation on AOSP exports a Wallet Manager HAL, custom SELinux domains, and a CI/CD pipeline that vet community modules before release. The result is not merely another hardware wallet but a programmable substrate that can evolve at the velocity of the blockchain ecosystem. By welding radical extensibility to hardware-anchored assurance, the platform closes the security-usability gap that has long stymied mass-market self-custody. We posit that modular TEEs are the missing OS primitive for Web3, much as virtual memory unlocked multi-tasking in classical computing. Together, these contributions sketch a blueprint for multi-chain asset management that is auditable, resilient, and poised for global deployment.
In today's world, e-government services are critical for assisting citizens with their daily activities such as visa applications, tax submission, emergency security assistance, and electronic tendering. By combining blockchain and IoT technologies, e-government services can be made far more secure and efficient. Existing e-government applications suffered from a number of limitations, including a lack of privacy and security, increased job processing time, a lack of coordination among various parties, and a lack of services. More specifically, they did not conduct simultaneous investigations into citizen service, employee service, and business service while comparing performance. To conquer these issues, this article proposes a decentralized blockchain-based secure and privacy-preserving smart e-government system that considers the interactions between informers, government, smart contracts, MetaMask-based public and private wallets, Ethereum, and the Interplanetary File System. We investigated the time and cost delays associated with employee, business, and citizen services in the proposed blockchain-based e-government system. This paper provides appropriate security measures for mitigating malware attacks, DDoS attacks, and Sybil attacks. Our simulation results show that the proposed blockchain-based e-government system can reduce the completion time of existing works by at least 33%. Received: 2 November 2024 | Revised: 6 February 2025 | Accepted: 23 May 2025 Conflicts of Interest The author declares that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Nahid Imtiaz: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Visualization. Mahfuzulhoq Chowdhury: Conceptualization, Methodology, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
WebAssembly has become the preferred smart contract format for various blockchain platforms due to its high portability and near-native execution speed. To effectively understand WebAssembly contracts, it is crucial to recover high-level type signatures because of the limited type information that WebAssembly provides. However, existing studies on type inference for smart contracts primarily center around Ethereum Virtual Machine bytecode, which is not applicable to WebAssembly owing to their differing targets and runtime semantics. This paper introduces WasmHint, a novel solution that leverages deep learning inference to automatically recover high-level parameter and return types from WebAssembly contracts. More specifically, WasmHint constructs a wCFG representation to clarify dependencies within WebAssembly code and simulates its execution to capture type-related operational information. By learning comprehensive code semantics, it infers parameter and return types, with a semantic corrector designed to enhance information coordination. We conduct experiments on a newly constructed dataset containing 77,208 WebAssembly contract functions. The results demonstrate that WasmHint achieves inference accuracies of 80.0% for parameter types and 95.8% for return types, with average improvements of 86.6% and 34.0% over the baseline methods, respectively.
Xing Su, Hanzhong Liang, Hao Wu, Ben Niu · 6 authors
Understanding the Ethereum smart contract bytecode is essential for ensuring cryptoeconomics security. However, existing decompilers primarily convert bytecode into pseudocode, which is not easily comprehensible for general users, potentially leading to misunderstanding of contract behavior and increased vulnerability to scams or exploits. In this paper, we propose DiSCo, the first LLMs-based EVM decompilation pipeline, which aims to enable LLMs to understand the opaque bytecode and lift it into smart contract code. DiSCo introduces three core technologies. First, a logic-invariant intermediate representation is proposed to reproject the low-level bytecode into high-level abstracted units. The second technique involves semantic enhancement based on a novel type-aware graph model to infer stripped variables during compilation, enhancing the lifting effect. The third technology is a flexible method incorporating code specifications to construct LLM-comprehensible prompts for source code generation. Extensive experiments illustrate that our generated code guarantees a high compilability rate at 75%, with differential fuzzing pass rate averaging at 50%. Manual validation results further indicate that the generated solidity contracts significantly outperforms baseline methods in tasks such as code comprehension and attack reproduction.
In smart contract development, practitioners frequently reuse code to reduce development effort and avoid reinventing the wheel. This reused code, whether identical or similar to its original source, is referred to as a code clone. Unintentional code cloning can propagate flaws and vulnerabilities, potentially undermining the reliability and maintainability of software systems. Previous studies have identified a significant prevalence of code clones in Solidity smart contracts on the Ethereum blockchain. To mitigate the risks posed by code clones, clone detection has emerged as an active field of research and practice in software engineering. Recent studies have extended existing techniques or proposed novel techniques tailored to the unique syntactic and semantic features of Solidity. Nonetheless, the evaluations of existing techniques, whether conducted by their original authors or independent researchers, involve codebases in various programming languages and utilize different versions of the corresponding tools. The resulting inconsistency makes direct comparisons of the evaluation results impractical, and hinders the ability to derive meaningful conclusions across the evaluations. There remains a lack of clarity regarding the effectiveness of these techniques in detecting smart contract clones, and whether it is feasible to combine different techniques to achieve scalable yet accurate detection of code clones in smart contracts. To address this gap, we conduct a comprehensive empirical study that evaluates the effectiveness and scalability of five representative clone detection techniques on 33,073 verified Solidity smart contracts, along with a benchmark we curate, in which we manually label 72,010 pairs of Solidity smart contracts with clone tags. Moreover, we explore the potential of combining different techniques to achieve optimal performance of code clone detection for smart contracts, and propose SourceREClone, a framework designed for the refined integration of different techniques, which achieves a 36.9% improvement in F1 score compared to a straightforward combination of the state of the art. Based on our findings, we discuss implications, provide recommendations for practitioners, and outline directions for future research.
Ethereum smart contracts determine state transition results not only by the previous states, but also by a mutable global state consisting of storage variables. This has resulted in state-inconsistency bugs, which grant an attacker the ability to modify contract states either through recursive function calls to a contract (reentrancy), or by exploiting transaction order dependence (TOD). Current studies have determined that identifying data races on global storage variables can capture all state-inconsistency bugs. Nevertheless, eliminating false positives poses a significant challenge, given the extensive number of execution paths that could potentially cause a data race. For simplicity, existing research considers a data race to be vulnerable as long as the variable involved could have inconsistent values under different execution orders. However, such a data race could be benign when the inconsistent value does not affect any critical computation or decision-making process in the program. Besides, the data race could also be infeasible when there is no valid state in the contract that allows the execution of both orders. In this paper, we aim to appreciably reduce these false positives without introducing false negatives. We present DivertScan, a precise framework to detect exploitable state-inconsistency bugs in smart contracts. We first introduce the use of flow divergence to check where the involved variable may flow to. This allows DivertScan to precisely infer the potential effects of a data race and determine whether it can be exploited for inducing unexpected program behaviors. We also propose multiplex symbolic execution to examine different execution orders in one time of solving. This helps DivertScan to determine whether a common starting state could potentially exist. To address the scalability issue in symbolic execution, DivertScan utilizes an overapproximated pre-checking and a selective exploration strategy. As a result, it only needs to explore a limited state space. DivertScan significantly outperformed state-of-the-art tools by improving the precision rate by 20.72% to 74.93% while introducing no false negatives. It also identified five exploitable real-world vulnerabilities that other tools missed. The detected vulnerabilities could potentially lead to a loss of up to $68.2M, based on trading records and rate limits.
Karim Moawad, Ahmad Musamih, Assia Chadly, Ahmad Mayyas · 8 authors
The urgency to combat climate change and reduce greenhouse gas emissions has led to increased global demand for Lithium-ion (Li-ion) batteries. Such batteries are widely used in portable electronics and electric vehicles. However, their adoption encounters challenges related to mining ethics, supply chain transparency, sustainability, and waste management. This paper proposes a blockchain-based solution that addresses these challenges in the Li-ion battery supply chain. Using the ERC-721 standard for Non-fungible tokens (NFTs), we tokenize all items/materials in the supply chain, ensuring data management, transparency, and ownership control. We integrate the Ethereum blockchain with the Interplanetary File System (IPFS) to handle NFT metadata and large-sized files, reducing storage costs and network congestion. We develop ten smart contracts (SCs) to facilitate various Li-ion supply chain functionalities, managing items/materials data and ownership. By leveraging NFTs, our solution promotes circular economy principles by facilitating secondary market trading, asset reuse, and sustainable recycling practices. We introduce a structured decision framework that empowers stakeholders to navigate operational and ethical challenges effectively. The effectiveness and practicality of the solution are demonstrated through system architecture, sequence diagrams, algorithms, and testing results. Furthermore, we assess our proposed solution’s affordability, efficiency, security, and generalizability across different industries.
Ranjit Kannappan, Julien Hatin, E. Bertin, Noël Crespi
The Digital Product Passport (DPP) is a key enabler of the European Union’s vision for a circular economy. Achieving the full potential of DPP requires addressing the challenges of traditional product lifecycle systems (PLM). Traditional PLM focuses on streamlining data management and decision making. However, their centralized architecture limits transparent, crossorganizational collaboration, impacting the circular economy efforts. This paper proposes a blockchain based framework, tailored to support DPP implementation by enabling the creation and sharing of lifecycle data using digital twin technology. The proposed architecture implements two types of digital twins - Component Digital Twin and Product Digital Twin modeled using the Asset Administration Shell (AAS) standard to ensure interoperability. The architecture leverages Ethereum smart contracts for blockchain interaction and IPFS for off-chain decentralized storage. Two approaches for secure data sharing are implemented: Direct and Signature-based data sharing. Performance evaluation shows low latency for key operations like twin creation (167 ms) and data sharing (64 ms). By leveraging decentralization in DPPs, the proposed framework fosters collaboration, transparency, and circular economy practices, empowering stakeholders to access and share critical product data throughout the lifecycle.
The distribution of consensus power is a cornerstone of decentralisation, influencing the security, resilience, and fairness of blockchain networks while ensuring equitable impact among participants. This study provides a rigorous evaluation of consensus power inequality across five prominent blockchain networks - Bitcoin, Ethereum, Cardano, Hedera, and Algorand - using data collected from January 2022 to July 2024. Leveraging established economic metrics, including the Gini coefficient and Theil index, the research quantitatively assesses how power is distributed among blockchain network participants. A robust dataset, capturing network-specific characteristics such as mining pools, staking patterns, and consensus nodes, forms the foundation of the analysis, enabling meaningful comparisons across diverse architectures. Through an in-depth comparative study, the paper identifies key disparities in consensus power distribution. Hedera and Bitcoin demonstrate more balanced power distribution, aligning closely with the principles of decentralisation. Ethereum and Cardano demonstrate moderate levels of inequality. However, contrary to expectations, Ethereum has become more concentrated following its transition to Proof-of-Stake. Meanwhile, Algorand shows a pronounced centralisation of power. Moreover, the findings highlight the structural and operational drivers of inequality, including economic barriers, governance models, and network effects, offering actionable insights for more equitable network design. This study establishes a methodological framework for evaluating blockchain consensus power inequality, emphasising the importance of targeted strategies to ensure fairer power distribution and enhancing the sustainability of decentralised systems. Future research will build on these findings by integrating additional metrics and examining the influence of emerging consensus mechanisms.
Abdulrahman Alzahrani, Amin Y. Noaman, Ahmed A. A. Gad-Elrab, Fathy Eassa · 7 authors
Interoperability among heterogeneous blockchain platforms remains a significant challenge in decentralized ecosystems, especially with the growing adoption of blockchain across various industries. This research introduces the Data Standardization Module (DSM). This framework facilitates seamless communication between platforms like Ethereum and Hyperledger Fabric using a unified schema for data transformation and exchange. DSM leverages Concise Binary Object Representation (CBOR) encoding, which reduces data size by 60 % and achieves a compression ratio (CR) of 2.5. This outperforms conventional JSON-based methods, typically achieving only 20–30 % compression with lower CR values. The system ensures data security and integrity through end-to-end encryption, access control, and validation mechanisms. Performance evaluations show that DSM supports an average throughput of 250 transactions per second (TPS). In comparison, interoperability frameworks like Cosmos and Polkadot typically achieve 100–150 TPS, while early implementations of Hyperledger Fabric reported TPS as low as 300. However, optimized settings of Hyperledger Fabric now achieve over 100,000 TPS. DSM strikes a balance by providing high throughput with minimal resource overhead, making it suitable for real-world applications. This work advances blockchain interoperability by offering a lightweight, secure, and scalable framework, ideal for high-frequency use cases in healthcare, finance, and supply chains.
Background: With the enhanced data amount being created, it is significant to various organizations and their processing, and managing big data becomes a significant challenge for the managers of the data. The development of inexpensive and new computing systems and cloud computing sectors gave qualified industries to gather and retrieve the data very precisely however securely delivering data across the network with fewer overheads is a demanding work. In the decentralized framework, the big data sharing puts a burden on the internal nodes among the receiver and sender and also creates the congestion in network. The internal nodes that exist to redirect information may have inadequate buffer ability to momentarily take the information and again deliver it to the upcoming nodes that may create the occasional fault in the transmission of data and defeat frequently. Hence, the next node selection to deliver the data is tiresome work, thereby resulting in an enhancement in the total receiving period to allocate the information. Methods: multi-node data repetition. Blockchain is involved in offering a transparency to the application of transmission. A simultaneous multi-threading framework confirms quick data channeling to various network receivers in a very short time. Therefore, an advanced method to securely store and transfer the big data in a timely manner is developed in this work. A deep learning-based smart contract is initially designed. The dilated weighted recurrent neural network (DW-RNN) is used to design the smart contract for the Ethereum blockchain. With the aid of the DW-RNN model, the authentication of the user is verified before accessing the data in the Ethereum blockchain. If the authentication of the user is verified, then the smart contracts are assigned to the authorized user. The model uses elliptic Curve ElGamal cryptography (EC-EC), which is a combination of elliptic curve cryptography (ECC) and ElGamal encryption for better security, to make sure that big data transfers on the Ethereum blockchain are safe. The modified Al-Biruni earth radius search optimization (MBERSO) algorithm is used to make the best keys for this EC-EC encryption scheme. This algorithm manages keys efficiently and securely, which improves data security during blockchain operations. Results: smart contracts.
Supply chain operations have tended to become more complex, thus placing significant pressure on one of the most critical processes: supplier selection and order allocation (SSOA). This process involves a focal company selecting suppliers and allocating orders to obtain required materials. Achieving effective SSOA processes is challenged by (1) reliance on centralized governance and (2) ensuring effective contract management. While so called “smart contracts” could address these challenges, design knowledge about such technology — particularly in the SSOA context — is underexplored in the literature. In this paper we design a smart contract for SSOA in supply chains. We conducted a design science research study and developed three core artifacts: (1) a mathematical description of SSOA; (2) a system model of actor interactions; and (3) SSOA-relevant algorithms. Utilizing the Ethereum blockchain, we demonstrated and tested our smart contracts through scenario analysis. We found that our design is feasible and highly likely to address centralization and effectiveness challenges in SSOA. This paper contributes to the literature by demonstrating how smart contract design focusing on SSOA can further enhance blockchain-driven business models. In addition, we offer prescriptive knowledge on developing smart contracts for SSOA in supply chains.
Abstract—File storage platforms face inherent challenges such as censorship, limited transparency, vulnerability to single points of failure, and restricted user control over data. To address these limitations, this paper proposes a decentralized file-sharing system that integrates the Ethereum blockchain with the InterPlanetary File System (IPFS). Our design leverages smart contracts to securely manage file metadata and enforce access controls, providing an immutable and tamper-resistant record of data ownership and permissions. IPFS is utilized for efficient, distributed file storage, enhancing scalability and availability. User authentication is handled through wallet-based cryptographic verification, eliminating reliance on centralized identity providers. Additionally, the system supports micropayment- based monetization via smart contracts, enabling direct and transparent transactions between content creators and consumers. The proposed platform delivers a secure, censorship-resistant, and user-empowered file-sharing environment consistent with the principles of Web3. Keywords- Blockchain, IPFS, Smart Contracts,
Maria S. Aguiar, Elvira Albert, Samir Genaim, Pablo Gordillo · 7 authors
Context: Superoptimization is a synthesis technique that, given a loop-free sequence of instructions, searches for an equivalent sequence that is optimal wrt. an objective function. Superoptimization of Ethereum smart contracts aims at minimizing the size of their bytecode and the gas consumption of executing the contract’s functions. The search for the optimal solution poses huge computational demands –as the search space to find the optimal sequence is exponential on the given size-bound – being the main challenge for superoptimization today to scale up to real, industrial software. Even if the underlying problem for finding the optimal solution is decidable, practical tools often prioritize efficiency over completeness. This means they might be implemented to find a sub-optimal solution or even time out. Objective: This work aims at leveraging superoptimization to a real setting: Ethereum blockchain. This paper proposes a neural-guided superoptimization (NGS) approach which incorporates deep neural networks using (supervised) learning into superoptimization to improve scalability by predicting: (1) if a sequence is already optimal and hence the search can be skipped; (2) the size-bound for the optimal solution in order to reduce the search space. Method: We have downloaded over 13,000 smart contracts deployed on the blockchain for training and testing the machine learning models, and a disjoint set with 100 of the smart contracts with more transactions to prove our scalability gains and impact for the Ethereum community. Results: Incorporating DNNs resulted in a 16x overall speedup (12x for gas) with only 12% optimization loss (14% for gas), or a 3-4x speedup with no optimization loss. For the 100 analyzed contracts, this approach reduced the average compilation time to 3 min per contract and achieved monetary savings of $1.24M. Conclusions: The integration of machine learning models mitigates several limitations of traditional superoptimization by drastically reducing execution times while maintaining most of the original optimization gains.
Particularly blockchain, distributed Ledger technology (DLT) has transformed many industries; supply Chain management (SCM) is one of the fundamental benefactor. This paintings gives FARMSUPPLY, a brand new technique the use of blockchain to enhance traceability and openness in the food supply Chain (FSC). using Ethereum blockchain and smart contracts, FARMSUPPLY guarantees confirmation of important characteristics at every supply chain level-from farmer to merchant. FARMSUPPLY provides seamless preservation and retrieval of transaction data-including photos and locations-by means of immutable ledger recording and InterPlanetary file system (IPFS) integration. FARMSUPPLY solves important FSC problems via encouraging openness and traceability, therefore promoting informed decisionmaking and building confidence among the stakeholders.The FARMSUPPLY concept is thoroughly reviewed on this examine, with precise interest to its ability to transform FSC operations, raise consumer confidence, and assist the food zone to be normally green and sustainable.
Kai-Yuan Chen, Kai-Hsin Chen, Jyh-Shing Roger Jang
We propose a profitable trading strategy for the cryptocurrency market based on grid trading. Starting with an analysis of the expected value of the traditional grid strategy, we show that under simple assumptions, its expected return is essentially zero. We then introduce a novel Dynamic Grid-based Trading (DGT) strategy that adapts to market conditions by dynamically resetting grid positions. Our backtesting results using minute-level data from Bitcoin and Ethereum between January 2021 and July 2024 demonstrate that the DGT strategy significantly outperforms both the traditional grid and buy-and-hold strategies in terms of internal rate of return and risk control.
Md. Nahidul Islam Opu, Md Shahidul Islam, Sara Rouhani, Shaiful Chowdhury
Blockchain-based software systems are increasingly deployed across diverse domains, yet a systematic understanding of their development challenges remains limited. This paper presents a large-scale empirical study of 497,742 issues mined from 1,209 open-source blockchain projects hosted on GitHub. Employing BERTopic, a transformer-based topic modeling technique, we identify 49 distinct issue topics and organize them hierarchically into 11 major subcategories. Our analysis reveals that both general software development issues and blockchain-specific concerns are nearly equally represented, with Wallet Management and UI Enhancement emerging as the most prominent topics. We further examine the temporal evolution of issue categories and resolution times, finding that Wallet issues not only dominate in frequency but also exhibit the longest resolution time. Conversely, Mechanisms issues are resolved significantly faster. Issue frequency surged after 2016 with the rise of Ethereum and decentralized applications, but started declining after 2022. These findings enhance our understanding of blockchain software maintenance, informing the development of specialized tools and practices to improve robustness and maintainability.
Jonas Ballweg, Amir Kafshdar Goharshady, Zhaorun Lin
We consider the classical problem of running a decentralized and trustless auction, using a smart contract, on a programmable block-chain such as Ethereum. In our setting, there are n bidders who have paid a deposit to join the protocol. Each bidder i can make a bid 1 ≤ bi ≤ m and our goal is to find the highest bid (maxi bi) and its corresponding bidder (argmaxi bi) in a publicly-verifiable manner. Each bidder must be unaware of others' bids when making their own and should not be able to change their bid after having committed to it. Additionally, and most importantly, we aim to provide privacy to the losing bidders, ensuring that their bids remain undisclosed. This is particularly crucial in use-cases with repeated auctions in which knowledge of the bids in the previous auctions can affect the bidders' strategies. Formally, the information gained by any observer, whether a participant in the protocol or not, should precisely consist of the winning bid and its bidder and nothing more. We present a novel yet simple protocol for private sealed-bid auctions on the blockchain. Our protocol is decentralized and trustless. It is also both time- and gas-efficient. Our approach takes O(log m) time and costs O(log m) units of gas for each bidder. It also guarantees observational determinism with respect to all losing bids.
Bu çalışmada ortaya ilk çıkarılan on kripto para getiri ve işlem hacimleri ile birlikte varil başına Batı Teksas (WTI) ham petrol getirileri arasındaki ilişki test edilmiştir. Analiz için 29 Nisan 2013 – 04 Ağustos 2024 arası günlük veriler kullanılmıştır. Çalışmada ampirik olarak Granger ve Toda Yamamoto Nedensellik Analizi' nden yararlanılmıştır. Her iki analize göre WTI ile Bitcoin (BTC) arasında negatif tek yönlü ilişkiye rastlanmıştır. Granger nedensellik analizine göre WTI ile Ethereum (ETH) arasında, Toda Yamamoto nedensellik analizine göre ise WTI ile Filecoin (FIL) getirisi arasında negatif çift yönlü bir nedensellik ilişkisi olduğu sonucuna ulaşılmıştır. Elde edilen bulgular enerji fiyatlarında yaşanan dalgalanmaların küresel finansal istikrara etkilerini ortaya koymuştur. Enerji piyasalarındaki sürdürülebilirlik hedefleri ile blok zinciri teknolojisinin çevresel etkilerini en aza indirgemek için uluslararası regülasyonların geliştirilmesi ve bütüncül politikalar oluşturulması gerekmektedir. Bu öneriler kripto para birimlerinin, enerji piyasalarından kaynaklanan volatiliteye karşı daha dayanıklı hale getirilmesi için stratejik bir yol haritası sunmaktadır.
The rise of cryptocurrencies over the past decade has transformed the global financial landscape, introducing new paradigms in investment, value storage, and monetary exchange. This study investigates the role of cryptocurrencies—specifically Bitcoin (BTC) and Ethereum (ETH)—as alternative investment assets within modern portfolio frameworks. As digital currencies continue to gain legitimacy and acceptance among retail and institutional investors, it becomes imperative to examine their financial performance, volatility characteristics, and correlation with conventional asset classes such as equities, bonds, and commodities. This research adopts a hybrid methodological approach, combining rigorous quantitative analysis with qualitative review. Using historical market data from 2015 to 2024, it evaluates key performance indicators such as average returns, standard deviation, Sharpe and Sortino ratios, Value at Risk (VaR), and maximum drawdown. It further explores the utility of cryptocurrencies in enhancing portfolio efficiency through diversification benefits, while also considering risk mitigation through dynamic asset allocation and rebalancing. The study extends beyond price metrics to include macroeconomic factors, such as inflation trends and monetary policy shifts, which influence crypto markets. It also addresses behavioral finance phenomena—including herd behavior, market sentiment, and media impact—that contribute to the observed volatility and price surges. The emergence of decentralized finance (DeFi), stablecoins, and central bank digital currencies (CBDCs) are also discussed to contextualize the evolving ecosystem and its implications for future investment strategies. Key findings indicate that while cryptocurrencies have historically outperformed traditional assets in terms of absolute returns, they also exhibit significantly higher volatility and downside risk. Despite these risks, their low to moderate correlation with conventional financial instruments enhances their value as diversification tools in modern portfolios. However, the study cautions that this benefit may diminish during times of extreme market stress when cross-asset correlations tend to rise. Moreover, the research highlights critical regulatory, technological, and environmental challenges associated with crypto adoption, including inconsistent global regulations, concerns over energy-intensive proof-of-work systems, and vulnerabilities in smart contracts. These factors underscore the need for robust governance frameworks and investor education to support sustainable growth in the digital asset market. In conclusion, the paper asserts that cryptocurrencies can serve as high-risk, high-reward components of a diversified portfolio, particularly for investors with higher risk tolerance and a long-term investment horizon. The future integration of cryptocurrencies into mainstream finance will depend largely on regulatory clarity, technological innovation, and the maturation of supporting infrastructure such as custody services, derivative markets, and institutional-grade investment vehicles