Blockchain Papers

Sajjad Alizadeh, Majid Khabbazian

Solana is recognized for its innovative Proof of History consensus mechanism, a cryptographic method that enables validators—participants responsible for verifying transactions—to efficiently record and order events without extensive communication, thus supporting high transaction rates. Despite its high-speed transactions capability, low cost transaction fees and significant market presence, it remains relatively underexplored in academic research. To address this gap, this paper uses graph-based modeling to analyze Solana’s transaction network. The analysis reveals several interesting key characteristics, including a high concentration of transactions among central nodes, a prevalence of unidirectional transactions, and a low graph density. Moreover, we observe a significantly higher transaction failure rate (approximately 20% compared to 0.1% on Ethereum) and a substantial proportion of zero-value transfers (around 7.6% versus 0.66% on Ethereum). These findings shed light on underexplored aspects of Solana’s ecosystem and provide insights that could influence future blockchain research and applications. The findings are particularly relevant for understanding behavior of blockchains with high transaction rates, and optimizing blockchain scalability and security.

Çağlar Sözen

Abstract Cryptocurrency markets have evolved into a vital segment of the global financial ecosystem, drawing considerable interest from both investors and regulatory bodies. Yet, their extreme price instability demands innovative strategies for risk mitigation and investment that diverge from conventional financial practices. This research focuses on analyzing the volatility patterns of leading cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)—by employing GARCH-family models such as GARCH, EGARCH, TGARCH, and CGARCH. Through a comparative evaluation of these models, the study identifies the optimal framework for characterizing cryptocurrency market volatility. Utilizing daily closing prices from Yahoo Finance (January 1, 2019, to January 8, 2025), the analysis reveals that TGARCH outperforms others for BTC, EGARCH for ETH, and CGARCH for BNB, underscoring the critical role of asymmetric volatility in these markets. This work advances existing research by offering a detailed comparison of GARCH-based approaches and practical insights for risk evaluation and portfolio optimization.

Anshul Wagh, Mangal Singhal, Rachana Patil

The conventional space title industry is overwhelmed by centralized recorders that force tall recharging expenses, limit possession rights, and show security vulnerabilities due to single focuses of disappointment.This paper presents Domyn, a decentralized space title commercial center leveraging blockchain innovation and NFTs (Non-Fungible Tokens) to empower genuine possession of advanced spaces.Built on the Ethereum blockchain, Domyn utilizes ERC-721 keen contracts to tokenize space names, permitting clients to mint, purchase, offer, and exchange proprietorship without mediators.Furthermore, IPFS (InterPlanetary Record Framework) guarantees decentralized capacity, making the framework censorship-resistant and tamper-proof [8].Through an in-depth investigation of its design, exchange stream, and savvy contract execution, we illustrate Domyn's capacity to supply security, straightforwardness, and productivity compared to conventional space enlistment centers.The paper assist presents execution measurements, test case comes about, and a security assessment to approve the viability of the proposed framework.We moreover investigate future upgrades, counting Layer 2 scaling arrangements and multi-chain interoperability, to progress gas proficiency and selection.

K. Vamshee Krishna, Ganesh Udara, Geethika Maison, Joshmika Katepaka

Blockchain technology has emerged as a transformative approach for secure legal document management, offering key advantages such as transparency, immutability, and enhanced security. This study presents a detailed examination of blockchain’s application in managing legal documents, aiming to modernize and streamline traditional document workflows. The paper begins with an in-depth discussion of blockchain fundamentals, highlighting its decentralized structure, cryptographic safeguards, and consensus protocols. The proposed system follows a well-defined methodology: applicants first submit their credentials, which are authenticated by educational institutions. These verified credentials are then stored in the InterPlanetary File System (IPFS) for decentralized file handling, while only their cryptographic hashes are recorded on the blockchain. This approach reduces storage costs and improves scalability. To evaluate performance, the system was tested using multiple consensus algorithms, including Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance. Results indicated that Proof of Stake delivers the best balance between speed and security. A functional prototype demonstrated notable improvements enhancing verification accuracy, reducing processing time, and minimizing manual intervention making the process far more efficient than conventional methods. The system achieved a transaction throughput of 1000 transactions per second and an average confirmation time of 5 seconds, significantly boosting efficiency for institutions and employers verifying credentials. Additionally, a comparative analysis with traditional methods showed superior performance in terms of security, speed, and costeffectiveness, supported by tamper-proof validation and reduced fraud risk. This research not only strengthens the trustworthiness of document verification but also paves the way for future innovations such as cross-chain interoperability, AI-powered fraud detection, and mobile-based verification, enhancing both accessibility and operational excellence in academic credential validation

Murali Krishna Pasupuleti

Abstract: This research paper explores the design and implementation of scalable blockchain protocols to enable a trustless digital society through decentralized finance (DeFi), governance, and secure digital identity frameworks. Motivated by the growing demand for transparency, autonomy, and data sovereignty in digital systems, this research introduces a hybrid protocol combining Proof of Stake (PoS) consensus with Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs). The study develops a theoretical mathematical framework and conducts extensive simulation-based evaluations using standardized metrics such as transaction throughput, AUC-ROC, RMSE, and nDCG. Comparative analysis against baseline architectures—Ethereum 1.0, Hyperledger, and Polkadot—demonstrates that the proposed zk-PoS protocol significantly improves throughput, reduces latency, and enhances identity verification accuracy. Regression and predictive modeling further confirm the system's scalability and reliability under varied network conditions. Forecasting models predict an increase in secure identity match rates over time, underscoring the protocol’s adaptability to real-world decentralized applications. The implications of this work are multifold: it advances blockchain scalability theories, lays a foundation for decentralized identity systems, and provides practical insights for deploying trustless governance and financial platforms. These contributions are pivotal for transitioning toward a decentralized, inclusive, and tamper-resistant digital ecosystem. Keywords: blockchain scalability, decentralized finance, trustless systems, zk-SNARKs, Proof of Stake, secure digital identity, decentralized governance, identity verification, blockchain protocols, cryptographic consensus

I Putu Bramasta Priadinata, I Gede Iwan Sudipa, Ni Putu Suci Meinarni, I Made Leo Radhitya · 5 authors

Cryptocurrency is a highly volatile digital asset that requires accurate predictive methods. This study compares the performance of three deep learning architectures LSTM, GRU, and Bi-LSTM in forecasting the prices of Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) using univariate historical data. Evaluation was conducted through regression metrics (RMSE and MAPE) and classification of price movement into five categories, ranging from very bearish to very bullish, assessed using a confusion matrix. The results show that GRU performed best for BTC (RMSE 974.72, MAPE 1.18%), while Bi-LSTM outperformed others for ETH and BNB (RMSE 43.19 and 6.83; MAPE 1.16% and 1.08%) and achieved the highest classification accuracy (55% and 52%). However, overall classification accuracy remains low, reflecting the complexity of cryptocurrency price patterns. The study is limited by its univariate approach without incorporating external variables. Its contribution lies in combining regression and classification evaluation, and it recommends exploring multivariate and ensemble models in future research.

Senior Financial Markets Dealer, Nassau, The Bahamas, Vladyslav Yakymashko

This article investigates the phenomenon of volatility clustering in the cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH), through empirical time-series analysis. The study employs quantitative methods, including GARCH modeling, to identify persistent patterns in the price fluctuations of the two leading digital assets. The analysis is based on trading data over an extended period, encompassing both phases of high market turbulence and periods of relative stability. Adopting an interdisciplinary approach that integrates behavioral finance, econometrics, and financial market theory, particular attention is given to identifying autocorrelation, memory effects, and the structure of market shocks. The findings demonstrate that volatility clustering in BTC and ETH significantly differs from similar phenomena in traditional financial markets, largely due to their speculative nature, asset novelty, and the influence of both institutional and retail participants. The identified patterns enhance risk profiling for crypto assets and may be applied in hedging strategies, automated trading algorithm development, and investment portfolio optimization. Additionally, the study highlights the importance of accounting for both micro- and macroeconomic factors influencing market behavior. The article is intended for researchers in digital finance, risk managers, analysts, investors, and anyone examining unstable assets in conditions of high uncertainty and a rapidly changing informational landscape.

Artem Chystiakov, Mariia Zhvanko

Transparency is one of the key benefits of public blockchains. However, the public visibility of transactions potentially compromises users' privacy. The fundamental challenge is to balance the intrinsic benefits of blockchain openness with the vital need for individual confidentiality. The proposal suggests creating a confidential version of wrapped Ethereum (cWETH) fully within the application layer. The solution combines the Elliptic Curve (EC) Twisted ElGamal-based commitment scheme to preserve confidentiality and the EC Diffie-Hellman (DH) protocol to introduce accessibility limited by the commitment scheme. To enforce the correct generation of commitments, encryption, and decryption, zk-SNARKs are utilized.

Benjamin Gillen, Rashmi Ranjan Bhuyan, Gourab Mukherjee, Austin Pollok

The Ethereum blockchain plays a central role in the broader cryptocurrency ecosystem, enabling a wide range of financial activity through the use of smart contracts. This paper investigates how individual Ethereum wallets responded to the collapse of FTX, one of the largest centralized cryptocurrency exchanges. Moving beyond price-based event studies, we adopt a bottom-up approach using granular wallet-level data. We construct a representative sample of Ethereum addresses and analyze their transaction behavior before and after the collapse using an explainable artificial intelligence (XAI) framework. Our proposed framework addresses data scarcity in high-resolution wallet-level daily transactions by employing a calibrated zero-inflated generalized linear fixed effects model. Our analysis quantifies distinct shifts in transaction intensity and stablecoin usage, highlighting a flight to safety within the ecosystem. These findings underscore the value of a bottom-up methodology for quantifying the user-level impact of blockchain-based shocks, offering insights beyond traditional price-level analysis through wallet-level data.

Darshana Rawal, Jan Seedorf, Bhimesh Patil

Abstract. Environmental surveillance, emergency response, and smart city planning all require the use of geospatial data, which includes satellite imagery, cartographic records, and real-time GPS coordinates. The high sensitivity and value of location-specific information make it unsafe to store and transmit it through conventional, centralized means, which can result in privacy breaches, unauthorized manipulations, and potential misuse. This paper aims to design and implement a secure, blockchain-based framework that blends AES (Advanced Encryption Standard) and RSA (Rivest–Shamir–Adleman) key management, which addresses these challenges. The aim is to guarantee strong data confidentiality by using symmetric encryption, and to use public-key cryptography for granular access control and secure key distribution. The proposed system uses Ethereum smart contracts to connect encrypted data references to a decentralized ledger, ensuring tamper resistance and auditability. In the proposed system, a Python-based FastAPI backend is responsible for data ingestion, cleaning, encryption, and blockchain interaction, while a React frontend can upload datasets, generate encryption keys, and retrieve access permissions. Modular microservices and well-defined APIs can seamlessly integrate various components, such as data processing scripts and on-chain contract logic, during development. The system's scalability is demonstrated by evaluating its performance against various dataset sizes, which involves metrics such as encryption overhead, blockchain transaction costs, and smart contract execution times. The practical usability of the system in actual scenarios is demonstrated through user acceptance testing, which is crucial for adoption in resource-limited environments. The results show the proposed crypto-enhanced blockchain framework can significantly enhance geospatial data security while still maintaining operational efficiency. Integration with zero-knowledge proofs may be explored in future work to enhance privacy, mitigate energy costs through alternative consensus algorithms, and enhance resilience in multi-network ecosystems through cross-chain interoperability.

Carlos Beis-Penedo, Rebeca P. Díaz-Redondo, Ana Fernandez-Vilas, Manuel Fernández‐Veiga · 5 authors

Collaborative machine learning in sensitive domains demands scalable, privacy-aware and access-controlled solutions for enterprise-grade deployment. Conventional federated learning (FL) relies on a central server, introducing single points of failure and privacy risks, while split learning (SL) partitions models for privacy but scales poorly because of sequential training. We present HLF-FSL, a decentralized architecture that combines federated split learning (FSL) with the permissioned blockchain Hyperledger Fabric (HLF). Chaincode orchestrates split-model execution and peer-to-peer aggregation without a central coordinator, leveraging HLF’s transient fields and Private Data Collections (PDCs) to keep raw data and model activations off-chain and access-controlled. On CIFAR-10, MNIST and ImageNet-Mini, HLF-FSL matches the accuracy of a standard server-coordinated FSL baseline while reducing per-epoch training time versus Ethereum-based baselines. Performance and scalability tests quantify the Fabric coordination overhead via a component-level breakdown of SDK-facing latencies and communication volumes; empirically, this overhead increases wall-clock epoch time while preserving the same accuracy-vs-epoch behavior as a FedSplit Learning baseline.

Anjana Nagaria, Chetan Shingadiya

Encouraging just, secure, and open election processes is a fundamental aspect of any democratic culture. Traditional and even modern electronic voting systems are plagued by persistent issues like the failure to provide anonymity for voters, forgery risks, scalability, and the absence of verifiable trust. This paper proposes a blockchain-based digital voting framework designed to address these systemic limitations by leveraging distributed ledger technology and smart contracts. The proposed solution offers end-to-end verifiability, vote immutability, and decentralized auditing mechanisms through a mobile-accessible platform built on Ethereum using Solidity and Hardhat, with Node.js and React.js for frontend interfacing. Experimental results demonstrate improved system scalability, resistance to tampering, and support for remote voting, while maintaining ballot privacy and affordability. The research also evaluates key performance indicators under various test scenarios, establishing the system’s effectiveness and practical relevance in real-world electoral environments.

Tri Stiyo Famuji, Alya Masitha, Maulana Muhammad Jogo Samodro, Galih Pramuja Inngam Fanani · 5 authors

Perkembangan teknologi genomik meningkatkan urgensi penanganan kerentanan sistem terpusat dalam mengelola data genetik manusia. Penelitian ini merancang sistem terdesentralisasi berbasis blockchain dan IPFS untuk meningkatkan keamanan, integritas, dan aksesibilitas data. Blockchain digunakan untuk pencatatan transaksi yang immutable dan manajemen akses dinamis melalui smart contract, sedangkan IPFS menyediakan penyimpanan terdistribusi berbasis hash kriptografik (CID). Desain hybrid memisahkan penyimpanan data mentah (terenkripsi homomorfik) di IPFS dengan manajemen akses di blockchain Ethereum. Antarmuka pengguna berbasis React.js dan Web3.js memungkinkan pengunggahan data terenkripsi, permintaan akses berbasis peran, dan pemantauan audit sesuai standar GDPR/HIPAA. Pengujian membuktikan sistem ini efektif mencegah akses ilegal dan memastikan keterlacakan data. Tantangan seperti efisiensi energi dan kepatuhan regulasi diatasi melalui sharding, protokol layer-2, serta mekanisme penghapusan data selektif. Kerangka kerja ini mendukung kolaborasi riset genomik lintas institusi secara aman. Penelitian lanjutan akan difokuskan pada optimasi efisiensi komputasi dan perluasan skala data.

Joel Sepúlveda, Amanda Lemette, Karla Ohler-Martins

The rise of cryptocurrencies and decentralised finance (DeFi) has fuelled a fast-growing digital assets economy with major environmental and financial implications. Proof-of-work (PoW) systems like Bitcoin demand high energy and emit large volumes of CO₂, while proof-of-stake (PoS) alternatives such as Ethereum and Cardano significantly reduce environmental costs. This paper analyses seven major crypto projects: Ethereum, Uniswap, Aave, Maker, Cardano, XRP, and Stellar. It focuses on their energy consumption, financial performance, and sustainability. The study proposes a novel sustainability scoring framework to support ESG-aligned investment and regulatory design. While PoW offers unmatched security, its environmental toll is unsustainable. PoS models show promise but face governance and scalability concerns. The study highlights the urgent need for sustainable innovation and regulatory differentiation to align crypto markets with climate goals, investor expectations, and long-term economic viability.

Mrs. Nutan Singh

The present study aims to analyze the return and risk performance ofselected cryptocurrencies in orderto find out which cryptocurrencies have small risks and large returns. The research time period is 2017 to 2022. The objective of this research is to compute and compare the risk and return performance of the selected cryptos. The findings of this research are that the risk is very high in Bitcoin compared to Ethereum, as shown in the data analysis, and Ethereum has high returns. Before starting an investment, it is better to look at the ability of Cryptocurrency assets to minimize risks and make sure that the investment objectives are for the long and short term.

Joel Poncha Lemayian, Hachem Bensalem, Ghyslain Gagnon, Kaiwen Zhang · 5 authors

Ethereum blockchain uses smart contracts (SCs) to implement decentralized applications (dApps). SCs are executed by the Ethereum virtual machine (EVM) running within an Ethereum client. Moreover, the EVM has been widely adopted by other blockchain platforms, including Solana, Cardano, Avalanche, Polkadot, and more. However, the EVM performance is limited by the constraints of the general-purpose computer it operates on. This work proposes offloading SC execution onto a dedicated hardware-based EVM. Specifically, EVMx is an FPGA-based SC execution engine that benefits from the inherent parallelism and high-speed processing capabilities of a hardware architecture. Synthesis results demonstrate a reduction in execution time of 61% to 99% for commonly used operation codes compared to CPU-based SC execution environments. Moreover, the execution time of Ethereum blocks on EVMx is up to 6x faster compared to analogous works in the literature. These results highlight the potential of the proposed architecture to accelerate SC execution and enhance the performance of EVM-compatible blockchains.

Arthur Gervais, Liyi Zhou

Smart contract vulnerabilities have led to billions in losses, yet finding actionable exploits remains challenging. Traditional fuzzers rely on rigid heuristics and struggle with complex attacks, while human auditors are thorough but slow and don't scale. Large Language Models offer a promising middle ground, combining human-like reasoning with machine speed. Early studies show that simply prompting LLMs generates unverified vulnerability speculations with high false positive rates. To address this, we present A1, an agentic system that transforms any LLM into an end-to-end exploit generator. A1 provides agents with six domain-specific tools for autonomous vulnerability discovery, from understanding contract behavior to testing strategies on real blockchain states. All outputs are concretely validated through execution, ensuring only profitable proof-of-concept exploits are reported. We evaluate A1 across 36 real-world vulnerable contracts on Ethereum and Binance Smart Chain. A1 achieves a 63% success rate on the VERITE benchmark. Across all successful cases, A1 extracts up to \$8.59 million per exploit and \$9.33 million total. Using Monte Carlo analysis of historical attacks, we demonstrate that immediate vulnerability detection yields 86-89% success probability, dropping to 6-21% with week-long delays. Our economic analysis reveals a troubling asymmetry: attackers achieve profitability at \$6,000 exploit values while defenders require \$60,000 -- raising fundamental questions about whether AI agents inevitably favor exploitation over defense.

Lucia Jović

Ovaj rad prikazuje razvoj prototipa blockchain sustava za nadzor nad antidoping pos- tupcima u sportu. Sustav je osmišljen kako bi povećao transparentnost, sigurnost i nepromjenjivost podataka u procesima testiranja sportaša. Korištenjem Ethereum blockchaina, pametnih ugovora i tehnologija poput Reacta, Flask-a i Web3.py, im- plementirane su funkcionalnosti za tri glavne korisničke uloge: agenciju, laboratorij i sportaša. Agencija može inicirati zahtjeve za testiranjem, laboratorij upisivati rezul- tate, a sportaš pregledavati ishode. Evaluacijom su identificirane prednosti u odnosu na postojeće sustave, ali i ograničenja koja mogu biti predmet budućih poboljšanja, uključujući autentikaciju korisnika, veću skalabilnost i primjenu naprednih kripto- grafskih metoda.

Da‐Hea Kim

ABSTRACT Using a text‐based measure of peer opinions constructed from cryptocurrency‐related social media posts, we find that peer opinions contain valuable information about the prices of cryptocurrency options. Bitcoin options exhibit a volatility smile, which becomes steeper when peer opinions become bearish. The risk‐neutral skewness of Bitcoin returns implied by options prices becomes more negative in times of bearish opinions. The predictability of peer opinions for Bitcoin option prices remains robust after controlling for momentum, volatility, demand pressures, news effects, and other sentiment measures, and exhibits no evidence of reversal over time. This effect is pronounced when Bitcoin attracts high investor attention, more diverse opinions about Bitcoin are expressed on social media, and Bitcoin options are more actively traded. We find similar results for Ethereum options.

Laith F. Jumma, Leila Sharifi, Parviz Rashidi

Blockchain technology has reshaped digital finance, enabling decentralized applications (DApps) on platforms like Ethereum. However, these innovations have also facilitated fraudulent schemes such as Ponzi schemes, which deceive users with false promises of high returns. These schemes cause financial losses and weaken trust in blockchain systems. Existing detection methods face key challenges, including limited labeled data, over-reliance on transaction history, and failure to identify scams early. To address these issues, we propose a framework that combines static and dynamic features of smart contracts for early Ponzi detection. Our feature set includes opcode patterns, developer behavior, temporal trends, and metadata, crafted to work independently of transaction data. We enhance feature representation using TF-IDF, CountVectorizer, and Word2Vec for deeper semantic understanding. These features are used to train multiple machine learning and deep learning models such as Random Forest, XGBoost, CNNs, and BiGRUs. A stacking ensemble with a neural meta-learner integrates predictions for improved performance. The model achieves 99% accuracy and an AUC of 0.9522 on a curated Ethereum dataset, handling class imbalance through oversampling and synthetic data generation. We also employ SHAP for model explainability, offering insights into feature importance and promoting transparency. Our framework is scalable and supports real-time monitoring of contracts, helping prevent financial damage by detecting fraud at deployment. This solution enhances the security and reliability of decentralized finance platforms.

Atefeh Zareh Chahoki, Marco Roveri

Ethereum smart contracts operate in a concurrent environment where multiple transactions can be submitted simultaneously. However, the Ethereum Virtual Machine (EVM) enforces sequential execution of transactions within each block to prevent conflicts arising from concurrent access to the same state variables. Although this approach guarantees correct behavior, it limits the ability of validators to leverage multi-core architectures for faster transaction processing, thus restricting throughput. Existing solutions introduce concurrency by allowing simultaneous transaction execution combined with runtime conflict detection and rollback mechanisms to maintain correctness. However, these methods incur significant overhead due to continuous conflict tracking and transaction reversion. Recently, alternative approaches have emerged that aim to predict conflicts statically, before execution, by analyzing smart contract code for potential transaction interactions. Despite their promise, there is a lack of comprehensive studies that examine static conflict detection and its broader implications in specific smart contracts. This paper fills this important gap by proposing a novel static analysis method to detect potential transaction conflicts in Ethereum smart contracts. Our method identifies read-write, write-write, and function call conflicts between transaction pairs by analyzing state variable access patterns in Solidity contracts. We implement a tool that parses contract code and performs conflict detection. Evaluation on a dataset of real-world Ethereum smart contracts demonstrates that our approach achieves high precision in identifying potential conflicts. By enabling proactive conflict detection, our tool supports further design of transaction scheduling strategies that reduce runtime failures, enhance validator throughput, and contribute to blockchain scalability.

N. Kemelbekov, Yenlik Begimbayeva, Olga Ussatova

In this article, we explore the complexities surrounding token issuance within blockchain networks and their integration with decentralized exchanges (DEXs). With the swift evolution of cryptocurrency and blockchain technologies, token issuance has become a prevalent means of funding initiatives and creating novel digital assets. This journey involves tackling a spectrum of technical and organizational hurdles, ranging from choosing the right token standard to crafting, testing, and deploying smart contracts on the Ethereum blockchain. Further, we explore the integration of issued tokens with decentralized exchanges, highlighting the importance of such platforms in enabling token trading without reliance on centralized intermediaries. The technical solutions required for this integration, along with considerations of the unique aspects of exchange protocols, are critically analyzed. We pay special attention to the ERC-20 standard for token creation, detailing the process of smart contract development and deployment on the Ethereum network. Additionally, the advantages and limitations of integrating tokens with DEXs are examined, providing a comprehensive understanding of both the opportunities and challenges within the rapidly evolving digital asset ecosystem. This study extends the current understanding of token dynamics by incorporating an in-depth analysis of scalability challenges, cross-chain interoperability, and the evolving regulatory landscape affecting token issuance and trading. By offering practical recommendations for overcoming identified hurdles, this research guides practitioners and policymakers in navigating the complexities of the decentralized finance (DeFi) space, making a significant contribution to the field of blockchain technology and digital finance.

Zhaorun Lin

Programmable blockchains have long been a hot research topic given their tremendous use in decentralized applications. Smart contracts, using blockchains as their underlying technology, inherit the desired properties such as verifiability, immutability, and transparency, which make it a great suit in trustless environments. In this thesis, we consider several decentralized protocols to be built on blockchains, specifically using smart contracts on Ethereum. We used algorithmic and cryptographic tools in our implementations to further improve the level of security and efficiency beyond the state-of-the-art works. We proposed a new approach called Blind Vote, which is an untraceable, secure, efficient, secrecy-preserving, and fully on-chain electronic voting protocol based on the well-known concept of Chaum's blind signatures. We illustrate that our approach achieves the same security guarantees as previous methods such as Tornado Vote [1], while consuming significantly less gas. Thus, we provide a cheaper and considerably more gas-efficient alternative for anonymous blockchain-based voting. On the other hand, we propose a new family of algorithms for private, trustless auctions that protect bidder identities and bid values while remaining practical for smart contract execution. We ensure trustlessness by running the auction logic in a smart contract, thereby eliminating reliance on any single trusted party. This approach prevents bid tampering, front-running, and collusion by enforcing immutability and decentralized verification of bids. The resulting protocol uniquely combines efficiency, trustlessness, and enduring bid privacy, offering a scalable and secure solution for blockchain-based marketplaces and other decentralized applications.

Naiema Shirafkan, Hamed Rajabzadeh, Marcus Wiens

The increasing integration of blockchain technology in supply chains has brought about significant challenges due to the volatility of cryptocurrencies, as it has become an essential aspect of customers’ risk considerations. This study addresses the problem of managing supply chain operations amid such volatility, focusing specifically on pricing, advertising, manufacturer subsidy, and cybersecurity strategies within a manufacturer-retailer framework involving two cryptocurrency-based retailers that have higher market capitalisation compared to others: Ethereum and Bitcoin. The proposed solution employs game theory – a simultaneous game and two Stackelberg games with either retailer as the leader – to identify optimal strategies based on the corresponding parameter values. Accordingly, the study uniquely delivers blockchain-related risks by applying game theory to analyze the decision variables, providing insights into competitive pricing adjustments and leadership strategies for the cryptocurrency-based retailers under varying volatility levels. Results demonstrate that retailer pricing strategies must adapt to changes in wholesale prices and to the difference in cryptocurrency volatility. It also identifies crucial subsidy levels for manufacturers and optimal strategies for retailers under different volatility conditions to sustain profitability and demand.