Hanouf Al Ghanmi, Sabreen Ahmadjee, Rami Bahsoon, Hayatullahi Bolaji Adeyemo
Blockchain smart contract technology has revolutionised various industries by automating agreements through immutable and self-executing logic, reducing reliance on third-party intermediaries. However, despite its transformative potential, existing research has predominantly focused on technical aspectsâparticularly securityâwhile largely neglecting human-in-the-loop concerns. Systematic efforts to explore these concerns from a human perspective have been limited which creates a gap in the literature. This study aims to address this gap by offering a comprehensive understanding of smart contracts from a human-centred perspective. To achieve this, we conducted a systematic literature review to examine human-related issues in smart contracts and their existing solutions. We found that concerns are primarily concentrated in two stages: development and interaction. During the development stage, issues arise in relation to programming languages, including complexity, readability and expressiveness, as well as the legality of smart contracts and their ethical and social implications. In the interaction stage, concerns focus on usability, human readability, trust, governance and cost. Additionally, we identified several quality attributes frequently associated with these concerns such as transparency, accountability, understandability, simplicity, learnability, compliance and fairness. We also uncovered new human-centred quality attributes that are overlooked in existing literature, such as explainability and interpretability. This research offers valuable insights for researchers, requirements engineers and designers by examining existing efforts to address human-centric concerns and proposing future directions and opportunities to improve smart contract design.
This research investigates the dynamics of connectedness among cryptocurrency and various risk factors, including oil price demand and supply shocks, EPU, GPR, and the ADS business conditions index using the quantile time-frequency connectedness approach. The findings reveal that cryptocurrency behaves as a net receiver of shocks in the short term but transitions to a net transmitter over the long term. Critical sources of both short- and long-term shocks are attributed to oil price demand, supply fluctuations, and GPR. However, during extreme events like the COVIDâ19 pandemic and the Russia-Ukraine war, cryptocurrency, oil shocks, and other indices alternately become net transmitters and receivers of shocks depending on time frames and quantile ranges. During periods of heightened market uncertainty, monitoring the interconnected behavior of these variables is critical for investors and policymakers aiming to predict market shifts and manage risks effectively.
To overcome the structural limitations of traditional hydroponic systemsâinefficient input management, lack of verifiable traceability, high energy consumption, and absence of adaptive optimizationâthis paper presents an innovative architecture that synergistically integrates Distributed Ledger Technology (DLT), Internet of Things (IoT), and Artificial Intelligence (AI) to optimize resource management in controlled hydroponic environments. The proposed architecture constitutes a hybrid DTL, IoT and IA system founded on six principles: radical distribution of trust, defense in depth, verifiable trust through cryptographic proofs, modularity, native interoperability, and scalability. It comprises a distributed intelligent sensor network, a low-cost edge computing cluster, optimized artificial intelligence modules, and a DLT infrastructure based on Hyperledger Fabric with Raft consensus. Experimental results, obtained through system simulation on a 100 mÂČ greenhouse and validated by partial prototyping, demonstrate robust operational performance: average latency of 847 ms from sensor to blockchain, throughput of 150 transactions per second, availability of 99.7%, support for 500 simultaneous sensors, and energy autonomy of 14 months. AI models achieve 96.3% accuracy in nutritional prediction, with pH prediction error of 0.08 units and EC error of 15 ”S/cm. DDPG orchestration converges after 45 days with stabilization of the reward function. Comparative analysis reveals significant advantages: 18% yield increase, 15% reduction in input costs, 22% decrease in energy consumption during peak pricing periods, and 40% improvement in total cost of ownership over 5 years.
Daria Schumm, Gabriel Stegmaier, Cedric von Rauscher, Katharina MĂŒller · 5 authors
Blockchains raise new privacy challenges, especially in Decentralized Identity (DI) and Self-Sovereign Identity (SSI) systems. Zero Knowledge Proofs (ZKPs) offer privacy, but only allow binary verification. Homomorphic Encryption (HE) enables flexible operations on encrypted data (e.g., addition, multiplication) but lacks comparison support. This paper addresses this gap by introducing a privacy-preserving comparison operation within HE, presenting the first comprehensive comparison of ZKP and HE as privacy-preserving mechanisms.
Executing biometric matching between two embedding vectors on the blockchain remains a challenging problem due to inherent privacy concerns and the computational constraints imposed by block gas limits. To address these challenges, we propose zk-SABER, a succinct blockchain-based biometric authentication scheme that allows constant proof size and verification cost with respect to the embedding vector length. Our design combines a Merkle Tree and a biometric matching algorithm within a zkSNARK circuit to prove that a userâs biometric trait matches one of the registered templates in an anonymous manner. To ensure compatibility with state-of-the-art Deep Neural Network (DNN) models, we introduce a complete quantization pipeline that converts floating-point embeddings into zkSNARK-friendly representations. Our experiment results show constant transaction gas cost and proof size, regardless of the embedding vector length, thereby demonstrating the practicality of zk-SABER for real-world blockchain environments.
The emergence of blockchain technology has spawned a broader discussion of designs for digital currencies, with Central Bank Digital Currencies (CBDCs) - digital forms of fiat currency - being one of them. An important feature of digital currencies is facilitating transactions without network connectivity, which can enhance the scalability of cryptocurrencies and the privacy of CBDC users. However, in the case of CBDCs, this characteristic also introduces new regulatory challenges, particularly when it comes to applying established Anti-Money Laundering and Countering the Financing of Terrorism (AML/CFT) frameworks. This paper introduces a prototype for offline digital currency payments, equally applicable to cryptocurrencies and CBDCs, that leverages Secure Elements and digital credentials to address the tension of offline payment support with regulatory compliance. Performance evaluation results suggest that the prototype can be flexibly adapted to different regulatory environments, with a transaction latency comparable to reallife commercial payment systems. Furthermore, we conceptualize how the integration of Zero-Knowledge Proofs into our design could accommodate various tiers of enhanced privacy protection.
Pierre Ghaly, Harald GjermundrĂžd, Ioanna Dionysiou
Blockchain tokenization ecosystems face significant challenges in complying with privacy regulations such as the General Data Protection Regulation (GDPR), particularly the âRight to Be Forgottenâ mandate. The immutable nature of blockchain conflicts with the requirement for data deletion, creating a fundamental tension between technological capabilities and regulatory compliance. This paper presents a novel cryptographic audit framework for implementing GDPR-compliant data erasure in configurable tokenization systems. Our approach leverages cryptographic key destruction, zero-knowledge proofs for audit trails, and automated smart contract mechanisms to achieve practical data deletion while preserving blockchain immutability. The framework introduces a triple-layer architecture separating on-chain and off-chain references from off-chain sensitive data, enabling verifiable data erasure through cryptographic âshreddingâ techniques. We demonstrate the frameworkâs effectiveness through detailed algorithms and present a proof of concept comprehensive audit mechanism that generates cryptographic proofs of successful data deletion without revealing sensitive information. Our solution addresses critical gaps in current blockchain privacy implementations and provides a practical pathway for regulatory compliance in tokenization ecosystems.
Blockchain Technology Applications and Security
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Background. The rapid advancement of digital technologies has introduced blockchain as a potential tool in public procurement contracts within the public sector. Smart contracts, particularly within civil law frameworks, have gained legislative recognition in jurisdictions such as France and several U.S. states. This development raises important questions about integrating blockchain-based smart contracts into governmental procurement systems, with a view to enhancing procedural transparency and operational efficiency, while acknowledging the limitations and dependencies on institutional frameworks. The central issue lies in clarifying the legal and technical implications of blockchain-based smart procurement contracts. The research examines their potential to streamline public procurement management and improve procedural efficiency, while recognising the need for legal safeguards that maintain administrative law principles and accommodate institutional constraints. Methods. This study adopts a comparative analytical approach, examining relevant legal provisions, technical requirements, and administrative practices across multiple jurisdictions. Various blockchain modelsâpublic, private, hybrid, and consortiumâare evaluated for their suitability in procurement processes. Legislative experiences regulating smart contracts are analysed to extract best practices and inform a cautious framework for public sector adoption. Results and Conclusions. The analysis indicates that blockchain-based smart procurement contracts may reduce bureaucratic delays and minimise human errors, while providing immutable records that can support accountability. However, successful implementation requires legal and institutional adjustments to address enforceability, liability allocation, interoperability, and data protection. A practical model illustrating each operational stepâfrom drafting to automated executionâis proposed, emphasising feasibility and legal compliance rather than assuming transformative effects. The study highlights the necessity of tailored legislation, standardised protocols, and targeted training for public officials to support the cautious integration of blockchain in public procurement contracting. These measures aim to guide the legally informed and context-sensitive adoption of smart contracts, contributing to sustainable digital transformation in public sector governance.
Gas optimization is a critical concern in the development of Ethereum smart contracts, with substantial implications for both cost-efficiency and security. This review systematically examines the latest peer-reviewed research on gas consumption in Solidity contracts, focusing on how micro-level decisions such as function implementation, data member usage, and storage patterns as well as macro-level architectural choices, including object-oriented structures like aggregation and inheritance, influence gas usage. Empirical findings reveal that persistent storage operations and cross-contract calls represent the highest gas expenditures, while optimization techniques such as struct and variable packing, use of immutables, and minimized storage access can yield significant savings. Object-oriented features, although beneficial for modularity, tend to increase gas costs if not carefully managed. The adoption of formal verification frameworks ensures the correctness of automated optimizations and prevents the introduction of subtle bugs. Furthermore, network-level gas price volatility underlines the need for continuous benchmarking and adaptive strategies. Overall, the review demonstrates that effective gas optimization requires an integrated approach, combining empirical measurement, codelevel best practices, formal guarantees, and awareness of evolving network conditions.
Miaoer Li, Yi Zhu, Yali Liu, Zexin Li · 5 authors
Smart contracts are self-executing programs on blockchains, critical for enabling efficient, secure, and reliable data exchange and value transfer. However, as their application scenarios expand, reliability issues have become a major bottleneck for blockchain development. Existing vulnerability detection methods often model smart contract source code as graph structures and use Graph Neural Networks (GNNs) for feature learning. Yet these methods over-rely on static execution flow features and ignore dynamic behavioral information of contract accounts in real runtime environments, limiting their ability to capture dynamic patterns and semantic details of contracts. To address these challenges, this paper proposes a graph attention network vulnerability detection model with global feature augmentation for smart contracts (GaGAT). Specifically, we first model key functions and variables in the contract source code as nodes, and execution flows as edges to construct a base contract graph. Then, we innovatively introduce global virtual nodes that integrate two types of information: contract categories and contract account behavioral features, including balance changes, Ether inflow/outflow, daily transaction frequency and single transaction duration. After contract graph contraction and feature transformation, we generate a feature matrix as input to the GaGAT model. Subsequently, we conduct vulnerability detection. Through a series of experiments, we provide empirical evidence of the superior performance of our proposed method compared to existing approaches in detecting six different categories of vulnerabilities.This study provides a new paradigm for cross-modal feature fusion for smart contract security analysis.
Issues in error handling may have critical consequences in blockchain software, ranging from silent execution with invalid states to denial of services due to unexpected crashes. This paper discusses the pitfalls of errors handling within blockchain frameworks written in Go such as Hyperledger Fabric, Tendermint Core (including its derivatives, e.g. CometBFT, Ignite), and other frameworks (e.g. Cosmos SDK), as well as the Ethereum implementation. Then, it explores how a static analysis approach can be applied for the automatic detection of such of issues, allowing to fix buggy code before deployment, i.e., when the code becomes difficult to patch being blockchain a trustless, distributed, and decentralized environment. Finally, we evaluate our analysis implementation within GoLiSA on a set of existing smart contracts and blockchain applications, empirically demonstrating the feasibility of the proposed approach.
This thesis investigates critical software delivery latency at a large fintech organization, where a modern micro-application architecture was severely bottle-necked by a legacy, manual, ticketing-based approval system. This hybrid environment created an acute organizational bottleneck, imposing high coordination burdens and unpredictable delays on globally distributed feature teams. Using an Action Research (AR) methodology, the study first established a high-friction baseline, measuring the median Lead Time for Changes (LTC) at 20.2 hours. The core intervention involved replacing the mandatory manual approval gate with a fully automated, self-service deployment model integrated directly into the Continuous Integration/Continuous Delivery (CI/CD) pipeline. The intervention successfully drove significant organizational efficiency, yielding a 69% reduction in LTC, dropping the median time from 20.2 hours to 6.2 hours. Concurrently, Deployment Frequency (DF) increased by 213% (from 47 to 100 releases per week). This improvement solidified the organization's position within the DORA elite performance tier. The primary practical guidance derived from this case study is that sustained software acceleration requires prioritizing the decentralization of control over the deployment trigger. This is achieved not merely through technical automation, but by deliberately eliminating all mandatory human coordination steps via external systems (e.g., tickets), relying instead on real-time visibility tooling integrated into the developer workflow. Additionally, and more importantly, this required a complementary organizational culture shift, which involved transfer-ring accountability for production stability directly from administrative roles, such as the Program Manager, to the autonomous development teams.
Cryptocurrency investment in India has quickly become a mainstream financial activity, but it is still highly prone to psychological factors that impact the decision-making of retail investors. This study examines the effect of personality traits on cryptocurrency investment behavior using the mediating variable of behavioral biases. Based on the Big Five Personality Model and the theory of Behavioral Finance, data were gathered from 716 Indian retail investors using a structured questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted to analyze the relationships among the variables. Results show that Openness to experience and Agreeableness significantly predict Availability Bias, whereas Extraversion and Agreeableness affect the Disposition Effect. The theoretical framework shows how bias-driven investment behavior in volatile markets such as cryptocurrency is triggered by personality-based predispositions. The study adds to the behavioral finance literature by taking psychological profiling outside the realms of traditional investment contexts into digital asset investing and provides practical insights for regulators, fintech platforms, and investment advisors to design interventions to mitigate bias and enhance investor education.
This presentation will demonstrate using WebAssembly for audio plugins outside the web browser. Specifically, the showcase is a WebAssembly binary used as a synthesizer plugin in a commercial, desktop based Digital Audio Workstation software such as Garage Band or Logic. In addition there will be a proposal and demonstration of a commercial model for distributing and controlling access and ownership to these WebAssembly audio plugins, by storing them on the NEAR blockchain and binding them to Non-Fungible-Tokens ( NFTs ).
Loyalty points can be used to encourage customers to make new purchases and play an important role in maintaining the existing customer base. In essence, this is a discount system, using which consumers can receive reward points after shopping or purchasing certain products. Loyalty points are a virtual currency that can be earned through certain shopping activities. According to a modern approach, loyalty points could also be exchanged for tokens based on blockchain technology. Tokens can be customized according to business needs, thus increasing the effectiveness of marketing. Since the tokens are created in the blockchain network, they are unforgeable, thus excluding the possibility of fraud or abuse. The purpose of the research is to examine whether âtraditionalâ loyalty points can be transferred to modern NFT-based tokens, thereby conveying uniqueness and unforgeability to consumers. As part of the practical implementation, the smart contract will be written using NFT (Non-Fungible Token) elements and the ERC 721 standard. However, to deliver consumer NFTs to their target, a smart contract-based airdrop-sending solution is also needed, which will be written in the research. On the company side, consumer NFTs are stored in an Ethereum-based sidechain before sending. As a further part of the practical implementation, a blockchain called PBTN (Private Blockchain Token Network) will be created by creating its genesis block. Until now, such a joint DAO-NFT(Decentralized Autonomous Organization) solution has not yet been implemented. The token loyalty point-based reward created in the crypto space is certainly a novelty these days.
R. Li, Srisht Fateh Singh, Andreas Park, Andreas Veneris
This paper presents a securities tokenization solution that brings the accessibility, transparency, efficiency, and innovation of blockchain and decentralized finance to real-world securities. Tokenization in principle seems straightforwardâan intermediary holds assets and issues 1:1 tokensâbut decentralized finance applications (DeFi) introduce significant complications. Even basic DeFi mechanisms, such as liquidity pools, pose challenges for tokenizing stocks and bonds because when assets are pooled in smart contracts, ownership becomes unclear, hindering asset owners to access their entitlements, such as dividends, coupons, or voting rights. Existing solutions often fail to address these challenges and are typically limited to specific security types. Our solution, by contrast, generalizes to any security and any holding rights through fungible tokens and using separate smart contracts for shareholders to redeem their entitlements. To address the decentralized ownership issue, our solution employs off-chain accounting with additional logic for liquidity pools. We implement this on Ethereum, demonstrating that it is 27% cheaper in gas costs than current alternatives. We also analyze the liquidity logic of over 90% of Ethereum's liquidity pools, confirming compatibility with our solution. Finally, we demonstrate its use for dividend-paying stocks, common stock, mergers, and coupon-paying bonds.
Blockchains are considered for healthcare data sharing due to their immutability, decentralization, and auditability. However, ledger transparency exposes on-chain identifiers and activity metadata, enabling linkage across pseudonyms and inference over user behavior. Prior work has primarily focused on content confidentiality and access control, while leaving identity unlinkability insufficiently addressed. To this end, we present an approach that integrates Account ion (AA), zeroknowledge proofs (Groth16), and Pedersen commitments. The approach embeds proof- and commitment-based verification into programmable smart contract accounts (SCAs), enabling authentication without disclosing identifiers and decoupling transactions from static keys. We develop a proof-of-concept on the Polygon Amoy testnet using Circom and Solidity, and evaluate privacy under a global, passive, external, static, and computationally bounded attacker. For the ERC-4337 comparison, the attacker is assumed to know user-SCA mappings; for the account-shuffling comparison, the attacker knows one SCA per user. Using entropy metrics and clustering-based inference over on-chain metadata, our approach achieves the maximum entropy of $\log _{2}(10) \approx 3.32$ in a ten-user setting (versus 0 for ERC-4337 as specified, i.e., without privacy extensions) and substantially reduces clustering accuracy relative to address shuffling (ARI $0.468 \rightarrow 0.038$, NMI $0.653 \rightarrow 0.177$), while maintaining the auditability required for healthcare governance.
Markus Jungnickel, Ferda Ăzdemir Sönmez, Catherine Mulligan, William J. Knottenbelt
Decentralized autonomous organizations (DAOs) have emerged as a novel organizational structure, attracting growing interest due to their decentralized, transparent governance, which replaces traditional hierarchies with stakeholder-managed rules codified as smart contracts. Although various governance models exist, comparative research across dimensions remains limited, leaving the literature fragmented and offering little practical guidance for selecting suitable models. This article critically analyses existing governance mechanisms and their implementation to support the development of more effective DAO models. To address current gaps, we review prior quantitative studies and conduct exploratory data analysis on centralization, participation, and decision controversy. The findings show that reputation and share-based models can mitigate the centralization seen in token-based systems, though all models suffer from low member engagement, suggesting an over reliance on direct democracy. Our analysis can be replicated across platforms and time frames to refine and validate these insights.
Christian ZeiĂ, Lisa Straub, Maximilian Greiner, Marcel Neis · 7 authors
Purpose To promote acceptance of blockchain-based investment options and enhance confidence for new investors, the market must become more comprehensible and accessible to the broad masses. This requires transparency to build trust in web-based intermediaries, particularly given the multitude of websites that often advertise unrealistic returns in the crypto sector. Consequently, intermediaries within the decentralized finance ecosystem need to be clearly identified and categorized to facilitate mass-market adoption. Design/methodology/approach We employ a six-iteration taxonomy approach, establishing a data foundation through literature reviews, expert interviews and document analysis of 50 intermediaries. Archetypes are derived using a hierarchical clustering algorithm. Finally, a survey is conducted to evaluate the taxonomy and the archetypes. Findings The taxonomy encompasses three meta-characteristics (functionality, architecture, security) and 63 characteristics. Furthermore, the research findings reveal six archetypes of blockchain-based investment intermediaries, demonstrating significant discrepancies between them, particularly in terms of financial features and governance structures. Given the complexity of crypto intermediary platforms for novice users, the findings underscore the need to implement technology-based and institutional-based trust mechanisms, improve risk assessment and enable informed decision-making. Originality/value By increasing market transparency and fostering trust, this study contributes to the acceptance and adoption of blockchain-based financial intermediaries, drawing on the diffusion of innovation theory. The proposed taxonomy, particularly its dimensions, specifically addresses the requirements of both technology-based and institution-based trust, which are critical for crypto investments. Moreover, the findings emphasize the importance of educational resources and communicated trust features in strengthening user confidence and facilitating broader market participation.
Seyed Ahmadreza Abtahi, Reza Abtahi, Bruno Rodrigues, B. Stiller
This demo paper presents DappTweet, a Web3 app that lets any blockchain address send posts and direct messages on Twitter/X via a MetaMask wallet using an addressproven posting workflow. A relay account publishes on the userâs behalf and embeds the senderâs address and the transaction hash for public verification. The prototype implements three flows: public post, private message, and verification.
Abstract Local Energy Communities (LECs) are gaining prominence as key actors in the transition toward sustainable and decentralized energy systems. A critical challenge for these communities lies in achieving energy self-sufficiency through effective forecasting of energy production and consumption. Accurate forecasting models are essential to support optimization and planning strategies. However, privacy concerns and regulatory constraints often limit the feasibility of centralized data-driven approaches, as users are understandably reluctant to share their consumption data. To address this issue, we propose a privacy-preserving forecasting framework based on Federated Learning (FL) and Long Short-Term Memory (LSTM) networks, which enables collaborative model training without disclosing raw user data. Building upon this core architecture, we further enhance transparency and user engagement by introducing Zero-Knowledge Proofs (ZKPs) for secure inference verification, and a novel incentive layer based on dynamic Non-Fungible Tokens (dNFTs) and fungibile tokens. Our approach ensures model integrity, protects user data, and fosters sustainable behavior through verifiable, trustless reward mechanisms. Experimental results demonstrate the feasibility and potential of this architecture in supporting privacy-aware, decentralized energy forecasting within LECs.