Diana Hawashin, Khaled Salah, Raja Jayaraman, Raja Wasim Ahmad · 6 authors
• Proposes a blockchain-based solution to reduce waste in the dairy industry • Ensures waste assessment and accountability through the proposed solution • Tests and validates various scenarios to evaluate the practicality of the system • Presents a security analysis to assess the system's resilience against threats In recent years, the global demand for dairy products has reached unprecedented levels in the food and beverage market. Despite being produced in large quantities to meet daily needs, high organic content in milk causes it to expire quickly, leading to food waste. Current dairy supply chain management systems lack traceability, auditability, and trust relationships, contributing to the problem. To address this issue, we propose a private Ethereum blockchain-based solution that holds all participants accountable and assesses their actions in a decentralized, auditable, traceable, secure, private, and trustworthy manner. Our system consists of various phases managed by four smart contracts that ensure all involved parties are accountable. We use an events-based approach to ensure traceability and data provenance, where all actions are stored on an immutable ledger in the form of events. By providing a transparent and traceable system, it encourages responsible resource utilization and supports a more sustainable approach to dairy production. We present the system architecture, sequence diagrams, entity-relationship diagrams, and algorithms to explain the working principles of our solution. We also validate the effectiveness of our developed smart contracts and make our smart contract code publicly available on GitHub.
Suleiman Dahir Mohamed, Mohd Tahir Ismail, Majid Khan Majahar Ali
Despite the introduction of several adjustments, mitigating data anomalies in financial datasets has proven challenging, particularly in the context of cryptocurrencies with extreme values and increased volatility. The progress in properly addressing these anomalies prior to testing remains restricted, highlighting the unique and complex nature of financial data in this domain. Thus, in this paper we propose a hybrid approach called the Win-IS strategy. It is meant to address the influence of extreme outliers in the tail and subsequently identify breaks, trend breaks and outliers in cryptocurrencies. This methodology uses the winsorization (Win) process to enhance the effectiveness of the indicator saturation (IS) approach. The study uses cryptocurrencies like Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Tether (USDT), and Ripple (XRP). The results of the research indicate that the winsorization strategy improved the detectability of the IS approach, with Win-IS outperforming the IS method in terms of the Bayesian Information Criterion. Furthermore, the Win-IS technique uncovered additional breaks, trend breaks and outliers that were previously unknown and repeated in some cases as detected by the IS strategy. The effect of winsorization is dependent on the chosen percentile and dataset attributes. Through detailed examination and comparison, the findings of this research contribute to the improvement of other detection approaches, providing a valuable perspective for researchers and practitioners in the field. Additionally, this hybrid approach can improve decision-making, risk management and model creation, benefiting investors, legislators and scholars.
In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. To address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we come up with attribute atomic matching smart contracts that are based on precise data property alignment. The goal is to get trading parties to match data attributes in a very specific way. During the trading execution phase, we use lightweight cryptographic algorithms based on Elliptic Curve Cryptography (ECC) and non-interactive zero-knowledge proofs to encrypt trading data twice and make attribute proof contracts. This keeps the data safe and private. The results of experiments conducted on the Ethereum platform in an industrial Internet of Things (IoT) scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection. Especially in battery industrial manufacturing, the application of distributed computing is in huge demand and essential to maintaining a healthier technology integration among various systems and technological nodes to perform the better management of energy cells within the battery management system.
Luiz Koodi Hotta, Carlos Trucíos, Pedro L. Valls Pereira, Mauricio Zevallos
Recent studies have suggested that more complex models than GARCH are better suited for forecasting cryptocurrency risk measures, such as Value-at-Risk and Expected Shortfall. Among these studies, some highlight the advantages of MSGARCH models over traditional GARCH models. While improvements over single-regime GARCH models have been observed by using MSGARCH, the literature has only focused on the MSGARCH specification proposed by Haas, Mittnik and Paolella (Journal of Financial Econometrics, 2004) overlooking several other well-established MSGARCH specification alternatives. In this paper, we illustrate that exploring alternative MSGARCH specifications can lead to improvements in risk measure performance, emphasizing the potential benefits of using several specifications.
This article provides a comprehensive analysis of the key risks associated with the operation of a crypto platform in the context of the transition to Web3 technology. The authors explore the activities of leading blockchain platforms such as Ethereum, Solana, Binance Smart Chain, Polkadot, Avalanche, Cosmos and Polygon, identifying the main types of risks that apply to the implementation of Web3 technology. The paper identifies threats associated with cryptocurrency volatility, regulatory uncertainty, cybersecurity, specific risks of decentralized finance (DeFi) and non-fungible tokens (NFTs), as well as scalability, accessibility and environmental issues. The authors analyzed the activities of the crypto platform and found that each platform has a unique structure and asset structure, which affects the nature of the risks. For example, Ethereum dominates the DeFi and NFT sectors, Solana is distinguished by its speed and low fees, Binance Smart Chain focuses on DeFi, and Polkadot and Cosmos are developing cross-chain technologies for interoperability. The main risks analyzed in the article include: Volatility of cryptocurrencies, which can increase financial instability; Cybersecurity, including hacking attacks and vulnerabilities of smart contracts; Regulatory uncertainty, which can hinder innovation and create legal conflicts; DeFi risks, such as errors in smart contracts, liquidation problems and systemic failures; NFT risks, in particular high market speculation and fraud risks; Scalability, including technical limitations and high fees; Complexity of use, which can limit the widespread adoption of Web3; Accessibility and inclusiveness, including unequal access to technologies; Energy consumption and environmental friendliness, which can affect the environment. To minimize these risks, the authors proposed a risk management strategy based on semi-fundamental principles: comprehensiveness, preventiveness, consistency, decentralization, transparency, security and interoperability. This strategy includes the introduction of modern analytical methods such as scenario analysis, machine learning, Value at Risk (VaR), Conditional VaR (CVaR), Monte Carlo models, multi-level security systems, bug bounty programs, transaction encryption, decentralized oracles, risk hedging using derivatives and RegTech solutions for regulatory compliance. For the effective implementation of the proposed strategy, a risk management roadmap was developed, which details the stages of risk identification, assessment, management and monitoring. This map includes specific tools for each stage, such as scenario analysis, AI analytics, blockchain scanners, VaR and CVaR models, attack models, stress testing, blockchain analytics, encryption, futures, options, RegTech, KPIs and behavioral models. The implementation of the proposed strategy will create a favorable environment for the reliable and sustainable development of Web3 technologies and the cryptocurrency market. Further research should be aimed at detailing platform-specific strategies and adapting them to the changing landscape of Web3.
Naveed Khan, OlaOluwa S. Yaya, Xuan Vinh Vo, Hassan Zada
In this paper, we examine the volatility and time-frequency connectedness among the financial stress index (FSI), cryptocurrencies namely, Bitcoin , Ethereum, Tether, BNB, Solana, and commodities namely, Gold, Silver, Copper, Platinum, and Brent Oil, using the quantile vector autoregressive (QVAR) frequency connectedness, wavelet coherence, and hedging effectiveness techniques, for the period spanning from June 2020 to December 2023. Findings indicate that the spillover effect among FSI, cryptocurrencies, and commodities substantially varies across different volatility conditions. Also, some cryptocurrencies are net receivers of shocks during normal market conditions, while other cryptocurrencies are net transmitters during extreme market conditions. We also find that, during the bullish market, some commodities (Platinum and Brent oil) are net receivers, while other commodities are net transmitters under extreme market conditions (lower quantiles). Similarly, findings further show that, under extreme volatility conditions (higher quantiles), cryptocurrencies and commodities are net receivers of shocks, while FSI is a net transmitter during these volatility conditions. Using frequency co-movement analysis, we find strong and weak correlations between these series in the short- and long-run for shorter periods. Furthermore, findings provide important implications for policymakers and portfolio managers to pay attention to long-term dynamics and design appropriate policies that mitigate the spillover effects.
While a plethora of machine learning (ML) models are currently available, along with their implementation on disparate platforms, there is hardly any verifiable ML code which can be executed on public blockchains. We propose a novel approach named LMST that enables conversion of the inferencing path of an ML model as well as its weights trained off-chain into Solidity code using Large Language Models (LLMs). Extensive prompt engineering is done to achieve gas cost optimization beyond mere correctness of the produced code, while taking into consideration the capabilities and limitations of the Ethereum Virtual Machine. We have also developed a proof of concept decentralized application using the code so generated for verifying the accuracy claims of the underlying ML model. An extensive set of experiments demonstrate the feasibility of deploying ML models on blockchains through automated code translation using LLMs.
This paper focuses on identifying recurring patterns among blockchain address categories using the FP-Growth algorithm, which is known for its efficiency in mining frequent itemsets within large datasets. The study provides insights into blockchain ecosystem dynamics by analyzing category associations across different blockchain networks like Ethereum and Bitcoin. Through this analysis, significant patterns were found, such as the frequent co-occurrence of categories related to smart contracts and exchanges, highlighting the central role of these categories in blockchain interactions. Additionally, the study delves into the influence of data sources on detected patterns, revealing that various data collection methods contribute to distinct biases, which affect category associations. The findings offer practical applications for blockchain analytics, such as improving classification models, anomaly detection, and enhancing regulatory compliance. This study contributes to blockchain research by showcasing how association rule mining can improve the categorization and understanding of blockchain address behaviors. The use of FP-Growth, as opposed to more traditional methods, enables faster and more comprehensive analysis, which is particularly valuable given the extensive nature of blockchain datasets. The research also points to potential directions for future work, such as integrating temporal data to observe changes over time and exploring additional blockchain networks to broaden the scope of insights. The study emphasizes the need for continuous advancements in blockchain address analysis to support security, transparency, and regulatory initiatives within this rapidly evolving digital ecosystem.
Blockchain technology has emerged as a transformative innovation, with applications spanning diverse industries. This study provides a comprehensive comparison between public and private blockchains, focusing on six key dimensions: scalability, security, use case distribution, energy efficiency, developer ecosystem, and performance metrics. Data were collected from 30 blockchain systems, representing a wide range of consensus mechanisms and industry applications. The findings reveal significant trade-offs between the two blockchain types. Public blockchains, such as Bitcoin and Ethereum, excel in decentralization and transparency, making them ideal for open and trustless environments like cryptocurrency and decentralized finance (DeFi). However, they face limitations in scalability, high energy consumption, and slower transaction speeds. Conversely, private blockchains, such as Hyperledger Fabric and Corda, demonstrate superior scalability, energy efficiency, and privacy, making them more suitable for controlled environments like healthcare, supply chain management, and enterprise financial services. The study underscores the importance of aligning blockchain technology selection with specific application requirements. Furthermore, it highlights the potential of hybrid blockchain models to integrate the strengths of both public and private systems, addressing existing limitations. These findings provide valuable insights for organizations and developers in leveraging blockchain technologies effectively.
In this work, we present a simple, uniform, and elegant solution to the problem, with stunning practical effectiveness and application to virtually any Datalog-based analysis. The approach consists of leveraging the choice construct, supported natively in modern Datalog engines like Soufflé. The choice construct allows the definition of functional dependencies in a relation and has been used in the past for expressing worklist algorithms. We show a near-universal construction that allows the choice construct to flexibly limit evaluation of predicates. The technique is applicable to practically any analysis architecture imaginable, since it adaptively prunes evaluation results when a (programmer-controlled) projection of a relation exceeds a desired cardinality. We apply the technique to probably the largest, pre-existing Datalog analysis frameworks in existence: Doop (for Java bytecode) and the main client analyses from the Gigahorse framework (for Ethereum smart contracts). Without needing to understand the existing analysis logic and with minimal, local-only changes, the performance of each framework increases dramatically, by over 20x for the hardest inputs, with near-negligible sacrifice in completeness.
The increasing adoption of blockchain technology has led to a growing demand for higher transaction throughput. Traditional blockchain platforms, such as Ethereum, execute transactions sequentially within each block, limiting scalability. Parallel execution has been proposed to enhance performance, but existing approaches either impose strict dependency annotations, rely on conservative static analysis, or suffer from high contention due to inefficient state management. Moreover, even when transaction execution is parallelized at the upper layer, storage operations remain a bottleneck due to sequential state access and I/O amplification. In this paper, we propose Reddio, a batch-based parallel transaction execution framework with asynchronous storage. Reddio processes transactions in parallel while addressing the storage bottleneck through three key techniques: (i) direct state reading, which enables efficient state access without traversing the Merkle Patricia Trie (MPT); (ii) asynchronous parallel node loading, which preloads trie nodes concurrently with execution to reduce I/O overhead; and (iii) pipelined workflow, which decouples execution, state reading, and storage updates into overlapping phases to maximize hardware utilization.
Minh Trung Tran, Nasrin Sohrabi, Zahir Tari, Qin Wang · 6 authors
We identify the slow liquidity drain (SLID) scam, an insidious and highly profitable threat to decentralized finance (DeFi), posing a large-scale, persistent, and growing risk to the ecosystem. Unlike traditional scams such as rug pulls or honeypots (USENIX Sec'19, USENIX Sec'23), SLID gradually siphons funds from liquidity pools over extended periods, making detection significantly more challenging. In this paper, we conducted the first large-scale empirical analysis of 319,166 liquidity pools across six major decentralized exchanges (DEXs) since 2018. We identified 3,117 SLID affected liquidity pools, resulting in cumulative losses of more than US$103 million. We propose a rule-based heuristic and an enhanced machine learning model for early detection. Our machine learning model achieves a detection speed 4.77 times faster than the heuristic while maintaining 95% accuracy. Our study establishes a foundation for protecting DeFi investors at an early stage and promoting transparency in the DeFi ecosystem.
Fei Ren, Bo Zhao, Jun Wang, Juxiang Zhou · 5 authors
With the rapid development of information technology, blended learning has become a crucial aspect of modern education. However, the fragmented use of various teaching platforms, such as Xuexitong and Rain Classroom, has led to the dispersion of teaching data. This not only increases the cognitive load on teachers and students but also hinders the systematic recording of teaching activities and learning outcomes. Moreover, existing blended learning evaluation systems exhibit significant shortcomings in large-scale data storage and secure sharing. To address these issues, this study designs a blended teaching evaluation management system based on blockchain and searchable encryption. First, an on-chain and off-chain collaborative storage model is established using the Ethereum blockchain and the InterPlanetary File System (IPFS) to ensure secure and large-scale storage of student work data. Next, a role-based access control scheme utilizing smart contracts is proposed to effectively prevent unauthorized access. Simultaneously, a searchable encryption scheme is designed using AES-CBC-256 and SHA-256 algorithms, enabling data sharing while safeguarding data privacy. Additionally, the smart contract comprehensively records students’ grade information, including weekly regular scores, midterm scores, final scores, overall scores, and their rankings, ensuring transparency in the evaluation process. Based on these technical solutions, a general-purpose teaching evaluation management system (B-Education) is developed. The experimental results demonstrate that the system accurately records teaching activities and learning outcomes, improving the transparency of teaching evaluations while ensuring data security and privacy. The system’s gas consumption remains within a reasonable range, demonstrating good flexibility and usability. Educational institutions can flexibly configure course evaluation criteria and adjust the weighting of various grades based on their specific needs. This study provides an innovative solution for blended teaching evaluation, offering significant theoretical value and practical implications.
Developing a computer program(smart contract) using reliability, a programming language for Ethereum blockchain. This smart contract will automate and secure the crowdfunding process by enabling druggies to contribute finances to a design, and the finances will be released to the design only when certain conditions are met, icing translucency and trust in the fundraising process. The design leverages the advantages of blockchain, similar as invariability and decentralization, to produce a more effective and dependable crowdfunding system. With vision of Government fund allocation through this platform. Developing a smart contract using reliability on the Ethereum blockchain to produce a secure and automated crowdfunding platform. The platform enables druggies to contribute finances to systems, with finances released only when specific conditions are met. This ensures translucency and trust, as all deals are recorded immutably on the blockchain. Decentralized governance allows contributors to share in backing opinions, enhancing the popular nature of the process. Also, the platform envisions integration with government fund allocation, furnishing a transparent and effective system for managing public finances and reducing the threat of corruption. Overall, the use of blockchain technology ensures a more dependable and responsible crowdfunding system. Harness the power of blockchain technology to produce a more effective, transparent, and secure crowdfunding platform. By automating fund operation through smart contracts and icing translucency through the Ethereum blockchain, we give a result that benefits both private systems and public fund allocation. This innovative approach has the implicit to transfigure crowdfunding and government backing, making fiscal processes more popular and responsible..
This paper applies the Lévy-GJR-GARCH model to explore the empirical dynamics of Bitcoin, Ethereum, and Ripple. It highlights volatility clustering, pronounced skewness, and high kurtosis in cryptocurrency markets. The study finds that models integrating innovation distributions more accurately capture and explain the volatility processes and tail risks in these assets. Advanced models, especially those accounting for extreme tail-end and asymmetric jump effects, are better suited for adapting to market changes and providing precise risk indicators, effectively identifying potential losses.
We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that, similarly to “financial LEGO bricks”, are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571) and discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.
Smart contracts, predominantly written in Solidity and executed on blockchains like Ethereum, are immutable, making functional correctness paramount: once deployed, bugs and vulnerabilities become permanent. Despite rapid progress in transformer-based code LLMs, existing evaluations of Solidity code completion rely heavily on surface-form metrics (e.g., BLEU, CrystalBLEU) or hand-grading, which poorly correlate with functional correctness. Unlike Python, Solidity lacks large-scale and execution-based benchmarks, hindering systematic assessment and optimization of LLMs for smart contract development. To bridge this research gap, we introduce SolBench, a comprehensive benchmark and automated testing pipeline for Solidity, designed to emphasize functional correctness via differential fuzzing. SolBench contains 28,825 functions from 7,604 contracts collected from Etherscan (genesis to 2024), spanning 10 popular domains. We benchmark 14 diverse LLMs (open/closed, 1.3B to 671B parameters, general/code-specific, with/without reasoning). The dominant failure mode is missing crucial details (e.g., type definitions, state variables) in intra-contract context. Providing full-contract context mitigates this and improves code completion accuracy. However, full-context inference can be prohibitively expensive in practice. Generating outputs with large context windows using state-of-the-art models often incurs significant costs, rendering naive context scaling economically impractical. Crucially, most of a contract is irrelevant to implementing a given function; only a small subset of details is needed. To exploit this, we propose Retrieval-Augmented Repair (RAR), which integrates retrieval into code repair: it uses the executor's error messages to extract only the most relevant snippets from the full contract. RAR sharply reduces input length for function completion, improving accuracy while significantly cutting computational cost. We further analyze retrieval and code repair strategies within RAR, showing substantial improvements in accuracy and efficiency. SolBench and our RAR framework enable principled evaluation and cost-effective improvement of Solidity code generation. Dataset and code are available at https://github.com/ZaoyuChen/SolBench.
As 6G networks evolve, inter-provider agreements become crucial for dynamic resource sharing and network slicing across multiple domains, requiring on-demand capacity provisioning while enabling trustworthy interaction among diverse operators. To address these challenges, we propose a blockchain-based Decentralized Application (DApp) on Ethereum that introduces four smart contracts, organized into a Preliminary Agreement Phase and an Enforcement Phase, and measures their gas usage, thereby establishing an open marketplace where service providers can list, lease, and enforce resource sharing. We present an empirical evaluation of how gas price, block size, and transaction count affect transaction processing time on the live Sepolia Ethereum testnet in a realistic setting, focusing on these distinct smart-contract phases with varying computational complexities. We first examine transaction latency as the number of users (batch size) increases, observing median latencies from 12.5 s to 23.9 s in the Preliminary Agreement Phase and 10.9 s to 24.7 s in the Enforcement Phase. Building on these initial measurements, we perform a comprehensive Kruskal-Wallis test (p < 0.001) to compare latency distributions across quintiles of gas price, block size, and transaction count. The post-hoc analyses reveal that high-volume blocks overshadow fee variations when transaction logic is more complex (effect sizes up to 0.43), whereas gas price exerts a stronger influence when the computation is lighter (effect sizes up to 0.36). Overall, 86% of transactions finalize within 30 seconds, underscoring that while designing decentralized applications, there must be a balance between contract complexity and fee strategies. The implementation of this work is publicly accessible online.
The fast development and growth of blockchain technology and cryptocurrencies, but most importantly, the fast diffusion of Ethereum, opened new chances for financial innovation but aggravated the risks of illegal activities such as money laundering. This paper discusses using machine learning techniques to detect illegal transactions over the Ethereum network. The dataset used is from Kaggle and includes a record of transaction features between Ethereum accounts; it has a high degree of class imbalance. Three machine learning models were used to classify transaction legality: Logistic Regression, Random Forest, and Extreme Gradient Boosting; this is referred to as XGBoost. Class balancing and data preprocessing are ways to improve model performance. The evaluation metrics were chosen as Accuracy and Area Under the Receiver Operating Characteristic Curve (ROC AUC). Experimental results show that the best performance of the XGBoost model was 98.52% in accuracy, while Random Forest was the best on ROC AUC, showing very strong classification capabilities. This work has shown the potentiality of machine learning in the improvement of blockchain security and provided useful lessons that might be applied to the development of scalable AML systems.
The research examines the increased popularity of electronic currencies as well as the radical change towards a digital economy.In the last decade, the use of cryptocurrencies like Bitcoin, Ethereum, and newly formed central bank digital currencies (CBDCs) has gained momentum, capturing extraordinary shifts in financial frameworks as well as in global economic models.The study examines the factors driving the growing adoption of digital currencies, noting their advantages including decentralization, improved security, lower transaction costs, and the capacity to enable cross-border payments.Aside from analysing the economic and technological drivers of digital currency adoption, the research looks at the general implications of digital finance on the existing banking systems, monetary policy, and regulatory regimes.The research also discusses the potential role of digital currencies in promoting financial inclusion, especially in areas with limited access to mainstream banking services.In addition, the research examines how the growth of the digital economy, typified by the convergence of digital currencies, blockchain technology, and decentralized finance (DeFi), is changing business models and consumer behaviours in various industries.The research, conducted through a mixture of surveys, case studies, and interviews with experts, cites major challenges including volatility, regulatory ambiguity, and security risks that may affect future stability and development of digital currencies. REVIEW OF LITERATUREDevlin (2019) -An Analysis of main and subsidiary credit card holding and spending.This research aims to investigate why the majority of multiple credit card holders hold a "main" card (i.e., one that is more frequently used than the others) and "subsidiary" cards (i.e., ones used less frequently or in an emergency situation) and the spending behaviour on main and subsidiary cards.
Perkembangan teknologi yang semakin berkembang banyak mengubah sistem yang telah ada diberbagai bidang, salah satu contoh perubahan yaitu sistem voting atau pemungutan suara. Di negara demokratisseperti indonesia sistem pemungutan suara sangat penting karena menjadi sarana masyarakat untuk menyuarakanhak-hak seperti contoh untuk memilih presiden atau wakil-wakil rakyat negara. Disamping itu sebagianpemungutan suara atau voting dilakukan dengan cara konvensional yaitu menggunakan kertas untuk menentukanpilihan sampai perhitungan hasil akhir suara, hal itu dapat menghabiskan biaya yang sangat banyak dan prosesperhitungan yang sangat lama dalam pelaksanaanya. Oleh karena itu, untuk menjawab permasalahan tersebutdirancanglah sistem e-Voting yang memanfaatkan teknologi smart contract dan blockchain. Dengan perjanjiandigital (smart contract) yang dibuat dengan bahasa pemrograman solidity, perjanjian tersebut tidak bisa diubahalurnya (paten) jika sudah diterapkan di blockchain. Setiap transaksi atau data suara pemilihan masuk maka akandilakukan hashing dengan menggunakan algoritma sha-256 (dimana sampai saat ini hash dengan sha-256 belumada yang mampu memecahkannya) dan kemudian akan membentuk suatu rantai block yang saling terhubung(peer-to-peer) di jaringan blockchain tersebut. Sehingga dengan memanfaatkan teknologi ini, data pemungutansuara yang telah dilakukan tidak dapat diubah, digandakan atau bahkan dihapus.
The certificate system is essential for academic organizations to provide proof of study or the level of skills and education. However, simply providing a physical cert or a virtual cert can be easily forged, and it will be difficult to be verified and authenticated. Many techniques are proposed to protect certificate’s authenticity such as Digital Watermarking Technology, RSA Digital Signature. Furthermore, there are also Blockchain approaches such as integration of existing system and private blockchain. However, those systems have weaknesses such as the vulnerability to be cracked and efficiency in verification of the certificate. The aim of this research is to provide a system that is capable of securing certificate authenticity from activities of certificate fraud. In this research, we proposed a blockchain e-certificate system for academic organization and public to issue and verify e-certificate with a simple web-based user interface. By combining the advantages of using decentralized ledger for key information and utilize IPFS to store the certificate file, it can solve the problem of the vulnerability of the existing system.
Krypto-Assets sind mit der zunehmenden Beliebtheit von Kryptowährungen ein verbreitetes Anlageprodukt geworden. Das Ziel der Arbeit besteht darin, das Konzept der Blockchain mit entsprechender technischer Umsetzung zu erläutern, die Investitionseigenschaften anhand einer Analyse aufzuzeigen und die Auseinandersetzung mit häufigen Kritikpunkten. Die empirisch‑quantitative gewonnenen Daten liefern im Betrachtungszeitraum von 01.02.2018 bis 31.01.2025 folgende Erkenntnisse: Kursentwicklung: Bitcoin (1010%) weist die höchste Performance auf und übertrifft damit Ethereum (211%) um das Fünffache, den S&P 500 (114%) um das Neunfache. Tether (2%) fokussiert keine Rendite, sondern Stabilität, dient daher nur als Referenz. Volatilität: Ethereum (1.9) hat den höchsten Spitzenwert für die rollierende Volatilität im 30-Tage-Fenster, gefolgt von Bitcoin (1.5). Einem vergleichbaren Bewegungsmuster folgen der S&P 500 (0.85) und Tether (0.15) und finden ihre Extremstelle ebenso im ersten Halbjahr 2020. Die deutlich geringere Schwankungsanfälligkeit des S&P 500s ist auf die höhere Diversifizierung durch die dahinterstehenden Wertpapiere zurückzuführen, bei Tether aufgrund der direkten Wertkoppelung an US-Dollar. Rendite-Risiko-Verhältnis: Bitcoin (35%) weist in der jährlichen Betrachtungsform die höchste annualisierte Rendite auf, gefolgt von Ethereum (15%), dem S&P 500 (10%) und Tether (0.003%). Die annualisierte Standardabweichung beschreibt das Risiko und wird von Ethereum (2.11) angeführt, darauffolgend Bitcoin (1.21), der S&P 500 (0.19) und Tether (0.008). Im sich daraus ergebenden Rendite-Risiko-Verhältnis führt der S&P 500 (0.39), danach folgen Bitcoin (0.26), Ethereum (0.04) und Tether (-3.21). Somit liefert der S&P 500 trotz geringerer Performance das beste Verhältnis aus Rendite und Risiko, was auf das deutlich geringere Risiko zurückzuführen ist. Korrelation: Bitcoin und Ethereum haben die höchste Korrelation (0.81), da beide als Kryptowährungen den gleichen Marktbedingungen ausgesetzt sind. Die Differenz zu 1 ist auf Einflüsse zurückzuführen, die das Asset selbst betreffen. Der S&P 500 korreliert leicht mit Ethereum (0.3) und Bitcoin (0.28). Die geringste Korrelation weist Tether auf, im Zusammenhang mit Bitcoin (0.01), dem S&P 500 (0.01) und Ethereum (0.02). Maximum Drawdown: Ethereum (90%) hat den höchsten Verlust im Vergleich zum Höchststand. Darauf, ebenso zu Jahresende 2019, folgt Bitcoin (70%), der S&P 500 (30%) zu Beginn des Jahres 2020 und Tether (5%) Ende 2019. Gesamtbewertung: Statistisch weist Bitcoin im Vergleich zu Ethereum höhere Renditen bei geringerem Risiko auf. Die geringere Korrelation von Bitcoin mit klassischen Anlageprodukten wie dem S&P 500 kann eine Diversifikationsfunktion begründen. Haftungsausschluss: Diese Thesis dient ausschließlich akademischen Zwecken. Trotz größter Sorgfalt bei der Erstellung kann keine Gewähr für die Richtigkeit und Vollständigkeit der enthaltenen Informationen übernommen werden. Der Autor übernimmt keine Haftung für Folgen, die sich aus der Verwendung dieser Arbeit ergeben. Disclaimer: This thesis is intended for academic purposes only. Although care has been taken to ensure the accuracy and completeness of the information, no guarantee is made that it is free of errors or omissions. The author assumes no responsibility for any consequences arising from its use.