Background: Health care has seen several new disruptive technologies. One such innovation is the introduction of blockchain smart contracts. These smart contracts are activated automatically once preprogrammed conditions are met. Smart contracts have improved patient outcomes, the efficiency of care delivery, and reduced costs. Despite their benefits, patients have had limited interactions with smart contracts in primary care; therefore, they may not trust blockchain-based smart contracts and may perceive them as risky or have concerns about their security. Objective: This study aimed to evaluate how patients' perceptions of smart contracts affect their adoption in primary care. Specifically, we investigated the impact of patients' perceptions of smart contract security, risk, and trust in their health care providers. Methods: This study used an experimental survey design to evaluate acceptance of smart contracts. Patients were randomly assigned to 1 of 2 research scenarios proposing either the positive use of blockchain smart contracts or the loss of benefits if a patient opted out. We collected data from a total of 387 participants. The Likert survey used 3 items to measure 5 constructs in the conceptual model. The 5 hypotheses were that gain-loss-framed messaging, perceived security, and trust would have a positive impact on the adoption of smart contracts, whereas perceived risk and the clinical setting would have a negative impact on patients' intention to adopt smart contracts. The conceptual model was tested using structural equation modeling, and the model fit indices suggested a good fit. Results: Most of the hypotheses were supported, except for the gain-loss-framing effect. As hypothesized, perceived security had the strongest positive influence on the intention to use smart contracts (β=.50; P<.001). Trust in health care providers also showed a significant positive relationship (β=.43; P<.001), while perceived risk had a smaller but still significant negative impact (β=-.071; P.048). Patients in clinic-based settings had a lower intention to use smart contracts than patients in telehealth settings, and male patients had a lower intention to use smart contracts than female patients. Conclusions: The results of this study have implications for health care providers who intend to adopt smart contracts early, that is, early majority or late adopters. To facilitate their implementation, providers should highlight the security benefits of smart contracts and leverage patient trust. Providers should customize smart contract implementation strategies based on patient demographics such as age, health status, and gender. By understanding these factors, health care organizations can more effectively promote the adoption of smart contracts and realize the potential benefits of this disruptive technology in primary care.
This paper addresses the challenge of resilient initial-dependent coordination in multi-agent systems with abnormal nodes. Initial-dependent coordination refers to the process where each node's final value converges to the transformed average of the initial values, with inter-node relationships modeled using augmented transformation matrices. This formulation captures a broad class of coordination and information fusion tasks involving coordinate transformations. We propose a resilient transformed consensus protocol and define the conditions required to achieve initial-dependent coordination in the presence of abnormal nodes. To implement these conditions, we design a distributed accounting and compensation mechanism. Specifically, each node maintains a private ledger that records real-time interaction data with its neighbors. Abnormal behaviors are detected by reconciling accounts with neighboring nodes, leveraging historical interaction information. The accounting mechanism provides a more flexible and effective detection condition. To recover from the impact of abnormal behaviors, we design a distributed compensation scheme that guides normal nodes to adjust their states, mitigating the adverse effects caused by abnormal nodes. Finally, numerical simulations in a sensor network under various abnormal behaviors validate the effectiveness of our approach.
Purpose This study aims to investigate how consumers respond to the brand crises caused by transactional Non-Fungible Token (NFT) price drops. Despite the emergence of NFTs as an innovative tool for brand marketing, numerous NFT projects fail to achieve their intended results, with some even experiencing a collapse in the secondary market, which can lead to brand crises. Although NFT price drops are inevitable, the academic understanding of how consumers respond to brand crises driven by NFT price drops remains limited. Design/methodology/approach Three preregistered experimental studies were conducted. Study 1 (n = 139) investigated the main effect of NFT price drops on brand attitude. Study 2 (n = 192) examined the mediating role of consumers' tolerance and brand responsibility. Study 3 (n = 338) further examined the moderating role of value cues. Findings The study reveals that when a brand crisis is caused by NFT (vs physical collectible) price drops, consumers show a more negative attitude. And in this process, consumers' lower tolerance for NFT price drops strengthens their perceptions of the brand's responsibility for the crisis. Moreover, when value cues for NFTs are enhanced, consumers' tolerance for price drops increases, which strengthens confidence in the serial mediation model. Originality/value This research contributes to crisis literature by addressing an overlooked source of brand harm: NFT crashes. It also extends NFT research by developing a theoretical framework that explains consumer psychological mechanisms amid digital asset crises. The findings further provide practical implications for brands operating in NFT markets and managing crisis recovery.
Consumer Behavior in Brand Consumption and Identification
Pharmaceutical supply chain is facing severe problems caused by counterfeiting medicines, unclear tracking system, and inefficient recalling method, which lead to huge economical losses and endanger the public's health. In this paper, we present a novel blockchain-based solution with Non-Fungible Token digital twin (NFT), which is based on Ethereum ERC-1155 standard, to construct an unchanged and transparent record for drug unit's whole lifecycle from manufacturing to final patient. Our system replaces the vulnerable centralized database with a decentralized one to guarantee the integrity of data, automatic compliance and immediate verification. Experimental results on Ethereum Sepolia testnet show that our system achieves 100% success rate for 9 transactions which are 7 transfer transactions and 2 creation transactions with average confirmation time just for 17.8s. The total operation cost for all transactions is only $0.01 USD which is very cost-effective. Base on the comprehensive analysis, our system can save 85-90% of operation cost and improve 95% of recalling time compared with traditional system. The experimental results in this paper prove the practical feasibility and feasibility of economy using NFT digital twin to manage the whole pharmaceutical supply chain, and provide a strong framework to fight against counterfeiting and ensure patient safety.
Shikah J. Alsunaidi, Hamoud Aljamaan, Mohammad Hammoudeh
Smart Contract (SC) vulnerabilities are programming errors or design flaws that can lead to financial loss or functional failure, making accurate detection essential. Although Machine Learning (ML) is widely applied to SC vulnerability detection, existing datasets are often small, imbalanced, inconsistently labeled, or nonstandardized, and frequently rely on limited feature representations that do not account for different contract lifecycle stages, restricting generalization and degrading benchmark reliability. This study introduces DIVE, a multi-label dataset that addresses these structural and feature-level limitations. DIVE includes 22,330 real-world SCs deployed between 2016 and 2024, and spanning major Solidity compiler versions, annotated for eight vulnerability types aligned with the Decentralized Application Security Project (DASP) Top 10 taxonomy. It provides 221 pre-deployment and 176 post-deployment features and employs a standardized multi-tool labeling pipeline based on Power-based voting and post-hoc filtering, which corrected 14.3% false positives in DoS and 24.9% in Time Manipulation. Unlike prior datasets, DIVE offers two lifecycle-specific feature sets and an open-source framework enabling reproducible benchmarking and periodic reconstruction aligned with evolving vulnerability patterns.
Voting is a critical process in any democratic country, but traditional methods like ballot papers and Electronic Voting Machines (EVMs) face several issues. These include a lack of transparency, low voter turnout, vote tampering, mistrust in the election process, voter ID forgery, delays in announcing results, and major security concerns. When it comes to digital voting, ensuring security is one of the biggest challenges, as the system must be capable of protecting data and preventing cyber-attacks. Blockchain technology offers a potential solution to these problems. It is a decentralized system that allows transactions to take place in a secure, peer-to-peer network. Blockchain key feature search as immutability, decentralization, security, transparency, and anonymity make it a strong option for developing secure and reliable e-voting systems. By using smart contracts, blockchain can further enhance the security and transparency of digital voting. This paper presents a sample e-voting application implemented as a smart contract on the Ethereum blockchain using Solidity. The system uses wallets with limited tokens (gas) that are consumed during voting, ensuring that each voter can only vote once. The paper also discusses the pros and cons of blockchainbased voting and demonstrates a basic web application to show its functionality and limitations.
ABSTRACT: Universal access to electricity remains one of the major structural challenges to development in sub-Saharan Africa, and particularly in the Democratic Republic of Congo (DRC), where territorial disparities and low rural electrification rates significantly hinder inclusive economic growth. Faced with the technical and financial limitations of traditional centralized grids, mini-grids and other decentralized electrification solutions are emerging as alternatives adapted to the country's geographical, demographic, and socio-economic realities. However, the development of these solutions fundamentally depends on the ability to mobilize appropriate, sustainable, and structured financing mechanisms. High initial infrastructure costs, combined with the limited repayment capacity of rural populations and a still-developing institutional environment, constitute major constraints to investment. The analysis highlights the need for a hybrid financial architecture, combining private equity, concessional debt, subsidies, and innovative financial instruments such as mezzanine debt, crowdfunding, and pay-as-you-go mechanisms. The economic sustainability of projects depends on a delicate balance between the financial viability of operators and affordable pricing for users. Business models must incorporate diversification of energy services, the integration of productive uses, and rigorous risk management (demand, exchange rate fluctuations, regulatory instability). The leverage generated by combining different funding sources strengthens investment capacity and improves project resilience. Institutionally, the regulatory framework plays a crucial role. The clarity of tariff rules, legal stability, transparency in subsidy allocation, and the effectiveness of rural electrification agencies are key factors in the sector's attractiveness to private investors. Tax and customs incentives, as well as risk guarantee mechanisms, are essential levers for reducing the cost of capital and stimulating local financial sector involvement. The study of the Congolese context reveals considerable energy potential, particularly in hydroelectric and solar power, but also persistent challenges related to access to credit, administrative complexity, and the structuring of public-private partnerships. Improving the financing of mini-grids in the DRC therefore requires an integrated approach combining regulatory reforms, institutional capacity building, and financial innovation. Ultimately, financing mini-grids is not merely a technical or budgetary issue, but a strategic challenge for energy governance and structural transformation. Establishing a coherent financial and regulatory ecosystem is essential to ensure the sustainability of projects, accelerate rural electrification, and contribute significantly to achieving the Sustainable Development Goals, particularly SDG 7 on access to reliable, affordable, and sustainable energy.
Ankit Kumar, Andres J. Aparcana-Tasayco, Minjung Kim, David Camacho · 5 authors
The expansion of Internet of Things (IoT) devices brings challenges of data security and privacy preservation in critical infrastructure. The proposed system combines blockchain technology with federated learning (FL) to secure IoT communications. It ensures decentralized model training with immutable and verifiable blockchain records. The study incorporates a lightweight FL model with a two-stage multitask head for binary and multiclass attack detection, enabling efficient deployment in constrained IoT. Federated learning effectively resolves privacy issues by facilitating cooperative model training across dispersed IoT nodes without revealing raw data. It guarantees collaboration based on a trust-aware mechanism that evaluates the reliability of clients and guides the robust aggregation. Blockchain ensures tamper-evident auditing of model updates and supports Byzantine-fault-tolerant (BFT) and Delegated proof-of-stake (DPoS) trust guarantees. Blockchain records cryptographic hashes of model modifications in a tamper-proof ledger, ensuring the legitimacy of the training process. Smart contracts enable the tamper-evident logging of model hashes, and global model convergence is ensured by federated averaging. Experimental tests demonstrate a secure and verifiable collaborative learning enabling model integrity in IoT networks. The study achieved a fast block generation time of 77.3ms, satisfactory model performance with 98% of training accuracy, and 0.992 F1-score alongside meaningful evolution of client trust values. The final testing accuracy of the FL model for the binary class detection is 98.1%. In a multi-class attack scenario, the FL model achieves a strong multi-class attack detection rate for dominant attack types.
As Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) expand, self-custody wallets have become the primary interface for user sovereignty. However, existing solutions suffer from critical limitations, including static authentication frameworks that compromise usability, a lack of real-time risk awareness, and inadequate key recovery mechanisms that often lead to permanent asset loss or reliance on centralized custodians. Furthermore, current wallets frequently expose transaction metadata, undermining user privacy. To address these systemic flaws, we present a modular self-custody wallet that incorporates a context-aware risk engine for real-time transaction scoring, risk-based adaptive authentication, and a dual-path decentralized key-recovery layer combining DAO-governed Shamir secret sharing with a zk-SNARK-verified fallback. The architecture further includes programmable policy enforcement and a zero-knowledge swap layer with stealth addressing to decouple front-end activity from on-chain data. The design integrates smart contracts on EVM chains and Solana through provider adapters and executes on-device ML inference to minimize latency. Experimental results demonstrate that the proposed system reduces privacy leakage probability to 5% (compared to 85% in standard architectures) and accelerates key recovery from over 24 h to approximately 8 seconds using zk-SNARKs, all while achieving 93.6% risk classification accuracy. The proposed CAPPR-Wallet advances self-custody by combining context adaptivity, privacy, and recoverability without centralized trust.
Open access
Blockchain Technology Applications and Security
Security and Verification in Computing
Physical Unclonable Functions (PUFs) and Hardware Security
Smart grids require real-time ancillary services from large-scale distributed energy storage (DES), creating a conflict between second-scale physical response needs and the slow confirmation of trust mechanisms like blockchain. Traditional VPPs lack scalability and trust for massive participation, while decentralized approaches struggle with mismatched time scales. We propose a framework that decouples real-time dispatch from asynchronous settlement. An off-chain matcher uses a physics-aware model, including a novel “service holding time” (Tservice) constraint and power (kW) envelopes, for fast assignments. A separate on-chain proof-of-stake (PoS) layer handles incentives and penalties (slashing) asynchronously. We formulate the MILP dispatch problem and provide a fast online heuristic alongside a MINLP decomposition benchmark. Co-simulations (IEEE 33-node) show that our scheme significantly outperforms baselines in success rate and latency, is robust against non-compliant nodes due to the PoS mechanism, and thereby offers a scalable and trustworthy solution.
Lukas Stopfer, Eugen Buss, Alexander Kaulen, Ferréol Berendt · 11 authors
This study quantifies the energy use, carbon dioxide equivalent (CO 2 e) emissions, and transaction-related costs of distributed ledger technologies (DLTs) in the context of timber traceability. It combines: (i) a PRISMA-guided systematic review of empirical studies on DLT energy consumption; and (ii) benchmark values derived from continuously updated online monitoring sources, captured at defined access dates and fully documented in the . Comparable metrics are reported at the level of individual traceability events (kWh/tx, gCO 2 e/tx, and USD/tx) and are related to a realistic timber supply chain transaction model that was empirically validated in a pilot study. The results reveal substantial differences in sustainability performance across consensus mechanisms. Proof-of-Work (PoW) networks exhibit prohibitively high energy demand and CO 2 e emissions for frequent traceability notarizations. In contrast, Proof-of-Stake (PoS), PBFT-based, hybrid, and Directed Acyclic Graph (DAG) architectures enable low-energy and low-cost event logging. This study bridges the gap between established DLT sustainability research and the operational requirements of regulated forestry traceability by providing a transparent and reproducible benchmarking workflow that includes URLs, access dates and calculation spreadsheets.
Understanding how Bitcoin mining is distributed across countries is important for evaluating both the sustainability and resilience of the network. In this study, we examine the evolution of total Bitcoin electricity consumption alongside the geographic distribution of Bitcoin mining. Data are provided by the Cambridge Centre for Alternative Finance (Licensed under CC BY–NC–SA 4.0): Annual data from the Cambridge Bitcoin Electricity Consumption Index (2010–2025) and a monthly panel of country-level Bitcoin hashrate shares for 105 countries (September 2019–January 2022). To assess the degree of decentralization in the global mining network, we employ entropy-based measures, inequality indices, and panel convergence tests. The results indicate that total electricity consumption grew exponentially during the early years of Bitcoin, but later transitioned to a more stable and approximately linear path. Country-level permutation entropy reveals highly volatile and dynamic mining trajectories. The Theil index shows that cross-sectional inequality declines over time, while increasing symbolic entropy reflects a progressively more even cross-country distribution of mining activity. Further evidence from σ-convergence supports a statistically significant reduction in cross-country dispersion of mining shares. Dynamic panel fixed-effects estimates reveal mean-reverting behavior in relative country shares, consistent with stochastic convergence. Finally, Phillips–Sul analysis points to heterogeneous early transition paths but ultimately supports convergence toward a single global club. The gradual geographical decentralization occurs alongside persistent core–periphery asymmetries in long-run mining shares. Overall, our findings suggest that Bitcoin mining behaves as a globally integrated industry in which computational capacity reallocates rapidly across countries in response to economic and regulatory conditions.
Purpose Sharing information is crucial for the success of supply chains. However, sharing information requires a careful balance between privacy and transparency. This study aims to explore the potential of zero-knowledge proofs (ZKPs) to improve this balance by enabling partial information sharing. Design/methodology/approach The authors apply a three-stage methodology to inductively generate a set of use cases for ZKPs in SCM. The authors expand and validate this set of use cases through a series of interviews and analyze the technology based on the use cases and further insights generated in the interviews. Findings The authors find that ZKPs can provide trust and privacy, increase speed and reduce costs across supply chain functions and relationships. The authors identify the two mechanisms responsible for these benefits and theorize on the relationship between the novel type of trust provided by ZKPs and interpersonal trust. Research limitations/implications This explorative study shows that ZKPs have the potential to make a substantial impact on SCM. They increase the attractiveness of information sharing and enable transactional relationships where more strategic relationships were previously required. However, their implementation and reliance on accurate input data require further investigation. Originality/value The authors expand existing literature on partial information sharing by investigating the partial sharing of one individual item of information. In doing so, the authors explore a novel technology with unique characteristics relevant to SCM. To the authors’ knowledge, they conduct the first study regarding ZKPs in SCM.
Alexander Kropiunig, Svetlana Kremer, Bernhard Haslhofer
Crypto Key Opinion Leaders (KOLs) shape Web3 narratives and retail investment behaviour. In volatile, high-risk markets, their credibility becomes a key determinant of their influence on followers. Yet prior research has focused on lifestyle influencers or generic financial commentary, leaving crypto KOLs' understandings of motivation, credibility, and responsibility underexplored. Drawing on interviews with 13 KOLs and self-determination theory (SDT), we examine how psychological needs are negotiated alongside monetisation and community expectations. Whereas prior work treats finfluencer credibility as a set of static credentials, our findings reveal it to be a self-determined, ethically enacted practice. We identify four community-recognised markers of credibility: self-regulation, bounded epistemic competence, accountability, and reflexive self-correction. This reframes credibility as socio-technical performance, extending SDT into high-risk crypto ecosystems. Methodologically, we employ a hybrid human-LLM thematic analysis. The study surfaces implications for designing credibility signals that prioritise transparency over hype.
Fatemeh Shoaei, Mohammad Pishdar, Mozafar Bag-Mohammadi, Mojtaba Karami
Smart contract-based ecosystems enable decentralized applications without trusted intermediaries, but their immutability and permissionless design also facilitate large-scale fraud. One of the most prevalent attacks is the rug pull, where project operators abruptly withdraw liquidity after artificially inflating token value. Existing detection methods primarily rely on reactive on-chain signals and often suffer from temporal data leakage, limiting their real-world reliability. This paper proposes a leakage-aware framework for early rug-pull detection that integrates on-chain behavioral metrics with temporally aligned Open Source Intelligence (OSINT) signals. We construct a hand-labeled dataset of 1,000 token projects, spanning DeFi and non-DeFi settings, with all features extracted strictly prior to any liquidity withdrawal to preserve causal validity. The dataset combines structural on-chain indicators with external attention signals derived from social media activity and search trends. Within this framework, TabPFN is employed as a core modeling component for learning from multimodal tabular data under strict temporal constraints. Experimental results show that the proposed framework achieves strong discriminative performance and improved probability calibration compared to classical baselines, while maintaining low false-negative rates. By framing rug-pull detection as a causal, multimodal forecasting problem, this work emphasizes the necessity of leakage-resilient evaluation and calibrated risk estimation for deployment in blockchain security systems.
This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and decentralization, respectively. While AI, particularly with the rise of large language models (LLMs), exhibits a strong centralizing force due to data and resource monopolization by large corporations, blockchain offers a counterbalancing mechanism through its inherent decentralization, transparency, and security. The editorial argues that these technologies are not mutually exclusive but possess complementary strengths. Blockchain can mitigate AI's centralizing risks by enabling decentralized data management, computation, and governance, promoting greater inclusivity, transparency, and user privacy. Conversely, AI can enhance blockchain's efficiency and security through automated smart contract management, content curation, and threat detection. The core argument calls for the development of ``decentralized intelligence'' (DI) -- an interdisciplinary research area focused on creating intelligent systems that function without centralized control.
Artificial Intelligence (AI) has become a critical driver of firm survival in the banking industry, particularly for deposit money banks (DMBs) facing increasing challenges such as economic volatility, regulatory compliance, cybersecurity threats, and rising customer expectations. This study explores the role of AI in enhancing operational efficiency, risk management, fraud detection, customer experience, and financial resilience in the banking sector. AI-powered technologies, including machine learning, predictive analytics, robotic process automation (RPA), and natural language processing (NLP), are transforming how banks analyze financial risks, detect fraudulent transactions, automate operations, and provide personalized banking services. Research findings indicate that AI adoption has led to a 35% reduction in loan defaults, a 40% improvement in operational efficiency, and a 60% decline in financial fraud cases, highlighting its transformative potential in ensuring the survival and competitiveness of DMBs. Despite these advancements, AI adoption in the banking sector is hindered by high implementation costs, cybersecurity vulnerabilities, workforce resistance, and regulatory uncertainties. Many banks, particularly in developing economies like Nigeria, struggle with legacy banking systems, lack of AI governance frameworks, and concerns over algorithmic bias in lending decisions. Additionally, AI-driven financial innovations, such as blockchain integration, decentralized finance (DeFi), and AI-powered ESG compliance solutions, are reshaping the banking industry, yet require strategic policy alignment and investment to maximize their benefits. The study identifies gaps in existing literature, including the need for empirical research on AI’s long-term impact on firm survival, its role in financial inclusion, and the ethical challenges of AI governance in banking. To bridge these gaps, future research should focus on developing AI implementation models suited to the challenges of emerging economies, exploring AI’s potential in expanding financial access to underserved populations, and strengthening AI-driven sustainability and ESG compliance frameworks in banking. As AI continues to evolve, deposit money banks must embrace a balanced approach that integrates AI innovation with regulatory oversight, cybersecurity safeguards, and workforce upskilling to ensure long-term survival and competitiveness in the digital financial landscape
The paper systematizes current regulatory and legal approaches across various jurisdictions, as well as theoretical and methodological recommendations proposed by scholars regarding the identification of different types of digital assets. It substantiates the hierarchical relationship among the concepts of "digital assets", "virtual assets" and "crypto-assets", which describe forms of digital value. The procedure for recognizing digital assets on the balance sheet is clarified. A three-tier classification of digital assets is proposed based on the following criteria: the mode of existence and circulation of digital value, the use of distributed ledger technology, and the mechanism for ensuring value stability. The study develops a sequence for accounting recognition of a digital asset as an intangible asset, a commodity, or a financial instrument, in compliance with accounting standards. It also justifies the classification of certain types of digital assets functionally similar to digital securities, which are recognized as financial instruments.
Abdullah Melhem, Ahmed Aleroud, Abdullah Al-Mamun, Mohamed I. Ibrahem · 5 authors
The use of web-enabled healthcare analytics has broadened access to machine learning (ML)- and AI-driven cloud models, but it has also created privacy and security challenges. Federated learning (FL) has been used to address data privacy issues; however, deployments of current FL architectures rely on centralized aggregation approaches, thereby creating a single point of failure (SPoF), as a successful adversarial attack on the global model during training or inference can compromise the entire system. These approaches also assume homogeneous data distributions across clients and overlook the constraints and diversity of web-based analytics. To address those limitations, traditional blockchain-based FL systems incorporated distributed ledgers to record model updates and artifacts. However, using the chain as a data ledger to record model artifacts and logs increases consensus overhead and coordination costs. This paper introduces Blockchain-based Clustered Federated Learning (BCFL), an architecture-diverse and cluster-based FL framework. Our approach is coordinated by a lightweight permissioned ledger that eliminates the trusted central aggregator while preserving utility, robustness, and verifiable provenance in web-based healthcare analytics. BCFL records compact provenance metadata on-chain while keeping model parameters off-chain. In addition, by distributing trust across clusters, the design reduces the transfer of adversarial attacks across models by limiting the impact of malicious updates during training and improving reliability at inference time. Experiments on real-world healthcare data and other benchmarks show that BCFL improves the performance of trained AI/ML models and reduces attack success rates compared with several FL baselines.
: Public blockchains enable decentralized applications but continue to face persistent challenges in scalability, privacy, and decentralization. This survey employs a structured and comprehensive literature review of 114 peer-reviewed studies and reputable technical reports (2018–2025), selected using predefined search strings, inclusion/exclusion criteria, and a structured screening process, documented using a Literature selection flow diagram. Scalability techniques—including sharding, Layer 2 architectures (e.g., ZK-Rollups, Optimistic Rollups, commit chains), and privacy-enhancing technologies such as zero-knowledge proofs (ZKPs), trusted execution environments (TEEs), and protocol-native mixers—are critically analyzed. Standardized benchmarking evaluates throughput, latency, gas efficiency, and decentralization under consistent test conditions. A key contribution of this study is the first integrated, datadriven assessment of privacy–scalability trade-offs within the blockchain scalability trilemma framework. Empirical benchmarking indicates, for example, that zkSync Era demonstrates a theoretical throughput of ~2000 TPS but achieves ~0.52 TPS under measured network conditions, highlighting the computational overhead of ZKP generation. Hybrid architectures—such as zkPorter’s off-chain data availability combined with ZKPs or TEE-based routing— consistently outperform single-layer approaches in balancing performance, confidentiality, and trustlessness. Post-2021 advancements, including modular rollups, MEV-resistant sharding, and machine-learning-based load prediction, are reviewed alongside open challenges in standardized benchmarking, post-quantum privacy systems, and compliance-aware PETs. Future research directions emphasize cross-layer designs integrating ZKPs, dynamic sharding, and regulatory- ready privacy protocols to enable secure, scalable, and legally compliant blockchain ecosystems.