This document provides a comprehensive prior art disclosure for the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The Y.I.N. Mazari Ordering establishes that for efficient cryptographic verification of differential privacy compliance, zero-knowledge proofs must be generated before encryption, not after. This disclosure documents extensions, variations, and applications of the ordering across: (1) all cryptographic primitives including post-quantum schemes, (2) all zero-knowledge proof systems, (3) diverse application domains including financial services, healthcare, and emerging technologies, and (4) various architectural configurations and trust models. The disclosure is published in the spirit of scientific contribution while establishing prior art for the described variations. Associated patent applications: U.S. Provisional Patent No. 63/923,348, U.S. Patent Application No. 19/399,646, and U.S. Continuation Application No. 19/403,244. Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Y.I.N. Mazari Ordering, Privacy-Preserving Machine Learning, Prior Art Disclosure
Keyword search is a fundamental technique for retrieving data outsourced to the cloud. Although encryption preserves data confidentiality, existing searchable encryption schemes often fail to efficiently support dynamic authorization and flexible retrieval. To address these limitations, we propose BAMKS , a blockchain-assisted attribute-based multi-keyword search scheme that supports secure and efficient search over version-aware encrypted data. In BAMKS , multiple data owners collaboratively generate version-bound access tokens that grant authorized users decryption privileges over evolving data. The scheme further enables conjunctive keyword search with updatable indexes. To ensure the integrity of search results, users can verify their correctness using an aggregated Schnorr-based non-interactive zero-knowledge proof, which is validated by smart contracts. In addition, BAMKS provides efficient attribute and user revocation without re-encrypting the stored ciphertexts, and supports user traceability for identifying malicious users from leaked keys. We formally prove that BAMKS achieves security against chosen-plaintext attacks (IND-CPA) and chosen-keyword attacks (IND-CKA) under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. Performance evaluations show that the scheme achieves lightweight decryption and efficient multi-keyword search, thereby reducing client-side computation and making it suitable for resource-constrained IoT environments. These features demonstrate the practicality of BAMKS for distributed cloud-edge-IoT storage applications.
The protection of sensitive assets through confidentiality, integrity, and accountability in distributed computing environments is one of the most important factors to consider because the threats to cloud infrastructures keep changing. To lessen these issues, the current work proposes a consensus based confidential data orchestration model (CD-COF) that employs demoralized trust enforcement and adapt meter of encryption that is used to bolster the safety of data. As in the suggested approach, every data item will be in a dynamic encryption cycle to integrate lattice-based post-quantum cryptography with the homomorphic encryption algorithm, allowing secure data storage and computation against aspiring cryptanalytic algorithms. The require cipher fragments or data is irreversibly encoded into a distributed registry replicated by cluster of validators in such a way that they result in tamper-proof inspection without revealing the data. To regulate access control, smart contracts authenticate zero-knowledge compliance proofs to ensure that a requester who wishes to gain access without supplying credentials does it. More importantly the ephemeral session tokens generated by verifiable random functions prevent attacks of re-play and collusion and a multi-dimensional trust scoring degree that a-dynamically reallocates access rights to the user, based on his or her usage habits. With the adaptive encryption and immutable anchoring implemented and verifiable access orchestration exploited, the new solution forms a robust and resilient security model in data protection of the decentralized domain of the cloud environment in the future. In it integrated domain of encoding, anchorage with ledgers and trust-based enforcement of access controls, the proposed CD-COF method has a total accuracy of 97 percent in protecting data at the cloud based.
Rui Wu, Guangfu Wu, Kangjun Li, Lei Liu · 6 authors
As blockchain technology matures, the application scope of smart contracts continues to expand. Security issues in smart contracts have become an important research focus. In view of the limitations of traditional vulnerability detection methods such as limited vulnerability types and high false positive rate, this paper proposes a smart contract vulnerability detection model based on deep learning. By constructing token sequence sets and data flow graphs and inputting them into the pre-training model of this article, we obtained a pre-training model optimized for Solidity code and implemented a multi-label classifier in the downstream detection layer. The proposed model can detect integer overflow vulnerabilities, transaction sequence dependency vulnerabilities, timestamp vulnerabilities, and reentrancy vulnerabilities in the target contract. Experiments show that this model is better than other detection tools in detection effect.
The applications of blockchain technology in a broad variety of contexts have become the basis of decentralized systems, and it has since grown much beyond its initial application to cryptocurrencies. This paper sheds light on two important bodies of research and provides an in-depth analysis of blockchain consensus algorithms. The former is the algorithm of Du et al. (2020) known as Mixed Byzantine Fault Tolerance (MBFT). Such MBFT illustrates a new solution to the fault tolerance and scalability problems of blockchain models of Ethereum and Bitcoin. Second, Chaudhry and Yousaf (2018) provided a clear analysis of consensus mechanisms. They went as far as to draw a comparative analysis of consensus algorithms, including but not limited to Byzantine Fault Tolerance (BFT), Proof of Work (PoW), and Proof of Stake (PoS). After conducting an extensive review and experimentation of each of the algorithms, relevant strengths, limitations, and suggestions on how to improve each of the analyses were brought to light in this paper. To summarize, the analysis given gives a platform to understand the area of future blockchain consensus research in security mechanisms, scaling solutions, and performance enhancement methods.
We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise → proof → encrypt → aggregate) is proven to be necessary—no efficient alternative exists—and universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning
Algorithmic stablecoins play a critical role in the Decentralized Finance (DeFi) ecosystem by aiming to maintain price stability without relying on traditional collateral reserves. However, these systems are prone to catastrophic failures known as death spirals, leading to irreversible price collapse and systemic instability. Despite increasing attention from both academia and regulators, existing approaches fall short in providing proactive prediction of such destabilizing events-an issue that poses serious risks to the sustainable development of the blockchain financial ecosystem. In this paper, we present SpiralSeer, a novel framework for fine-grained prediction of death spiral risks in algorithmic stablecoins. At its core, SpiralSeer introduces a stage-wise risk model that integrates on-chain user behaviors and off-chain market data, formalizing distinct states in risk evolution. We then develop a LightGBM-based risk detection model capable of detecting vulnerabilities in emerging stablecoins, and incorporates an additive feature attribution mechanism to reveal the most influential factors contributing to risk across different stages. Extensive evaluation demonstrates that SpiralSeer achieves a $27.8 \%$ improvement in precision over state-of-the-art baselines, and can flag early risk stages well before catastrophic decoupling events. By enabling early risk identification, SpiralSeer offers a practical foundation for building more resilient algorithmic stablecoin systems and enhancing risk transparency in DeFi.
The Internet Computer (IC) is a secure, fast and efficient decentralized blockchain-based platform for the execution of general-purpose applications. It has been operational since May 2021, running over 900K smart contracts and serving over 1M users.; AB@Over time, the IC has grown significantly in the number of smart contracts, their size, and the application scope, including social media, messengers and even games. In contrast, most other blockchain platforms focus on simple applications related to token transfers and Decentralized Finance (DeFi). In Fall 2024, a proof-of-work (PoW) blockchain was deployed on the IC. This blockchain grew quickly and reached over 20,000 miners in just days, creating operational challenges for other applications on the IC.; AB@Initial operational data showed degraded performance, but in a few weeks, the IC execution layer was fine-tuned and enhanced such that the PoW blockchain and other unrelated applications can now be supported in tandem with good quality of service. The IC's modular design allowed for targeted changes without protocol modifications. The changes concerned the operating system optimizations and scheduling algorithm tweaks. This article presents the journey navigating these with insights from operational data.
The accelerating digitalization of critical national infrastructures has underscored the urgent need for sovereign control over data, trust, and governance in cyberspace. Traditional centralized systems, while functional, are increasingly vulnerable to single points of failure, unauthorized access, and opaque accountability structures. Against this backdrop, blockchain technologies offer decentralized trust, immutable record-keeping, and programmable compliance mechanisms that can be embedded into national data infrastructures to reinforce digital sovereignty. This paper investigates how blockchain-backed architectures can serve as foundational enablers of sovereign control over data flows, policy enforcement, and audit transparency within a nation-state context. Using a multi-layered methodology that combines policy–technology mapping, comparative analysis of governance frameworks, and case studies across e-government, healthcare, and energy utilities, the study introduces a sovereignty-by-design blockchain framework tailored for Malaysia and ASEAN member states. Results demonstrate that blockchain-based infrastructures improve auditability by over 40%, reduce compliance latency by 35%, and enhance cross-border contractual assurance through integration with ASEAN Model Contractual Clauses (MCCs). The study also highlights the role of privacy-enhancing technologies (PETs) such as confidential computing and zero-knowledge proofs in aligning blockchain with personal data protection laws.
Federated learning (FL) enables collaborative model training across edge devices without centralizing raw data, but existing frameworks remain ill-equipped to support data privacy regulations mandated by GDPR, HIPAA, and CCPA. Once user data has influenced training, its verifiable removal becomes prohibitively expensive, particularly in non-IID and resource-constrained edge environments. This paper introduces a modular and scalable federated unlearning framework that unifies three complementary strategies: gradient subtraction, knowledge distillation, and checkpoint rollback, within an adaptive decision layer. A resource-aware checkpoint manager reduces storage costs through compression and pruning, while a privacy and trust layer integrates zero-knowledge proofs, differential privacy, and Merkle-based audit logs to provide verifiable guarantees of deletion. A non-IID-aware aggregator further preserves fairness across heterogeneous clients. Unlike prior approaches, our proposed framework systematically integrates rollback efficiency with formal privacy protections and auditability, offering a practical path toward trustworthy and regulation-compliant unlearning in domains such as healthcare, transportation, and smart agriculture.
Zhi-Yong Liang, Gaoyuan Liu, Ren Yi, Ming Yang · 7 authors
With the accelerated development of the global digital economy, data spaces have become a crucial infrastructure for cross-domain data circulation and value creation. However, cross-organizational and cross-regional data sharing still faces several challenges, including insufficient trust, fragmented governance, and inconsistent standards. Against this backdrop, blockchain technology, with its decentralized, traceable, and tamper-resistant characteristics, offers new avenues for building collaborative trust mechanisms within trustworthy data spaces. This paper systematically reviews the current research on trustworthy data spaces, the blockchain, zero-knowledge proofs, and federated learning. It proposes a technology-governance-standardization (TGS) framework for cross-border governance. To verify the framework, we proposed a collaborative trust mechanism combining “on-chain light attest, off-chain deep store, and cross-layer verifiable bridge” (LPHS–XV), which achieves data availability without visibility and compliance auditability. A prototype was then validated in the cross-border medical data space at the Macao-Hengqin Station, providing a scalable experience for global data governance.
The increasing demand for real-time decision analytics in modern enterprises has accelerated the development of edge-to-cloud data pipelines, which integrate distributed computing resources to enable instantaneous insights. Traditional centralized cloud architectures struggle with latency and bandwidth limitations, making them unsuitable for applications requiring immediate decision-making. Edge-to-cloud pipelines overcome these barriers by combining localized data processing with cloud-based intelligence, creating a continuous, adaptive flow of analytical information. This review examines the architectural principles, technological enablers, and analytical impacts of edge-to-cloud data pipelines on real-time decision-making. It explores how distributed processing, stream analytics, and AI-driven orchestration enhance responsiveness, reliability, and scalability across diverse environments. Technologies such as 5G, machine learning, and containerized orchestration platforms are discussed as key drivers of this transformation. The study also identifies challenges including data synchronization, security, interoperability, and energy efficiency at the edge. Addressing these issues is essential for realizing seamless, end-to-end analytics across hybrid ecosystems. Future directions highlight the potential of autonomous, decentralized, and quantum-enhanced data pipelines to deliver self-optimizing intelligence at global scale.Ultimately, this review concludes that edge-to-cloud data pipelines are foundational to achieving context-aware, predictive, and autonomous analytics, enabling organizations to transition from reactive operations to real-time, intelligent decision ecosystems.
Smart contracts are a core component of blockchain technology and are widely deployed across various decentralized scenarios. However, atomicity violations have become a critical potential security risk. Existing analysis tools often lack the precision required to detect these issues effectively. To address this challenge, we introduce AtomGraph, an automated framework designed for detecting atomicity violations. This framework leverages Graph Convolutional Networks (GCN) to accurately identify atomicity violations through multimodal feature learning and fusion. Specifically, driven by a collaborative learning mechanism, the model simultaneously learns from two heterogeneous modalities: extracting structural topological features from the bytecode-based Control Flow Graph (CFG) and uncovering deep semantics from its opcode sequence. We designed an adaptive weighted fusion mechanism to dynamically adjust the weights of features from each modality to achieve optimal feature fusion. Finally, GCN detects graph-level atomicity violations on the contract. Comprehensive experimental evaluations demonstrate that AtomGraph achieves 96.88% accuracy and 96.97% F1 score, outperforming existing tools. Furthermore, compared to the baseline concatenation fusion model, AtomGraph improves the F1 score by 6.4%, proving its potential in smart contract security detection.
Blockchain is a decentralized distributed ledger technology in which consensus algorithms ensure data consistency and system security among mutually untrusted nodes. The choice of a consensus algorithm significantly affects key performance indicators of a blockchain system, such as transaction throughput and energy consumption. This paper presents a systematic review of blockchain consensus algorithms. Based on their underlying principles, mainstream consensus algorithms are categorized into two major types: proof-based and voting-based mechanisms. Proof-based algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), rely on resource investment to obtain the right to produce blocks; voting-based algorithms, including Raft and Practical Byzantine Fault Tolerance (PBFT), achieve consensus through node collaboration and voting. This paper provides an in-depth analysis of nine representative consensus algorithms and compares them in terms of efficiency, scalability, fault tolerance, and complexity. Finally, the paper summarizes the current challenges and potential research directions in the field, offering theoretical support and reference for the design of future blockchain systems.
Blockchain has become peer-to-peer immutable distributed ledger technology network, and its consensus protocol is essential to the management of decentralized data. The consensus algorithm, at core of blockchain technology (BCT), has direct impact on blockchain's security, stability, decentralization, and many other crucial features. A key problem in development of blockchain applications is selecting the right consensus algorithm for various scenarios. Ensuring scalability is the most significant drawback of BCT. The industry has been rejuvenated and new architectures have been sparked by the usage of consensus protocols for blockchains(BC). Researchers analyzed shortcomings of proof of work (PoW) consensus process and subsequently, alternative protocols like proof of stake (PoS) arose. PoS, together with other improvements, lowers the unimaginably high energy usage of PoW, making it protocol of time. In PoS, only the user with highest stake becomes the validator. To overcome this, we propose Scalable Proof of Stake (SPoS), a novel consensus protocol, which is an enhancement of PoS protocol. In the proposed algorithm, each stakeholder based on the stake gets a chance to become the validator and can mine blocks in the blockchain. Clustering of the stakeholders is done using mean shift algorithm. Each cluster gets a different number of blocks to mine in BC. Cluster with highest stake will get a greater number of blocks to mine when compared to other groups and the cluster with the least stake gets least number of blocks to mine when compared to other groups. To mine the blocks, validator is chosen based on the cluster in which he is present. Fair mining is ensured for all stakeholders based on number of stakes. Mining is distributed among all the stakeholders. Since the validators are chosen fast, the transaction rate is high in the network. Validators in PoS are selected according to the quantity of cryptocurrency they stake. More stakeholders will get chance of validating blocks and receiving rewards. Over time, this reduces fairness and decentralization by concentrating on wealth and power. This is addressed in SPoS using clustering-based validator assignment.
This paper challenges the prevailing assumption in Central Bank Digital Currency (CBDC) design that comprehensive transaction surveillance is necessary for financial stability and crime prevention. We propose an alternative privacy-preserving architecture that achieves equivalent or superior fraud detection through mechanism design rather than identity monitoring. Key contributions: Separation of pattern detection from identity: Transaction graph analysis identifies structural anomalies without accessing participant identities Transaction-level intervention: Suspicious activity flags individual transactions, not accounts or users Opt-in deanonymization: Identity revelation is always voluntary; users may abandon flagged transactions without consequence Architectural enforcement: Privacy guarantees are structural, not policy-dependent The framework inverts the burden of proof in financial surveillance. Rather than requiring users to demonstrate legitimacy, it requires the system to demonstrate suspicion—and even then, users retain the option to walk away. This creates a game-theoretic deterrent where illicit actors cannot complete transactions, while legitimate users experience minimal friction. We demonstrate that privacy-preserving CBDC architecture is technically feasible using established cryptographic primitives (zero-knowledge proofs, secure multi-party computation, threshold cryptography) and that the choice to implement surveillance infrastructure represents a policy decision rather than technical necessity. Part of the Adversarial Systems Research program investigating friction dynamics in complex systems where competing interests generate structural conflict.
Sana Ullah, Syed Muslim Jameel, Meghann Drury-Grogan, Mara Sintejdeanu · 5 authors
The complexity of cross-border regulatory compliance in the MedTech sector imposes significant administrative and financial burdens on manufacturers, characterized by manual processes, data redundancy, and country-specific, cross-border heterogeneous regulations. To address this, we present EireLedger, a decentralized framework that automates and cryptographically enforces regulatory compliance verification. EireLedger utilizes a novel dual-purpose zero-knowledge proof (ZKP) scheme, instantiated with Groth16 zk-SNARKs, which allows a manufacturer to prove a device dossier's compliance to a jurisdiction-specific regulator in a privacy-preserving manner, while simultaneously generating a verifiable ZKP-based access grant for the regulator. This cryptographic proof is immutably anchored to a permissioned Hyperledger Fabric blockchain, which orchestrates the protocol and maintains a minimal, auditable record. The corresponding encrypted dossier artefacts are stored off-chain in a private IPFS cluster. Our comprehensive evaluation demonstrates that on-chain proof verification is highly efficient with a median latency of 12.3 ms, and our integrated ZKP-as-access-control model reduces end-to-end audit latency by 40% compared to traditional attribute-based access control (ABAC) by eliminating external authorization calls. The on-chain storage footprint is constant at ~2.1 KB per audit, ensuring data minimization. The framework also supports right to erasure in compliance with GDPR, cryptographically unpinning a 5 GB dossier in under 90 s. These results establish EireLedger as a novel, privacy-preserving, and practical solution for cross-border regulatory compliance in the MedTech supply chains.
Blockchain Technology Applications and Security
Big Data and Digital Economy
Physical Unclonable Functions (PUFs) and Hardware Security
K. C. Krishnachalitha, Dikshit Sharma, Samaksh Goyal, K. Yuvaraj · 6 authors
The present research combines Blockchain, 5G, and Green Computing to create secure, energy-efficient, and sustainable digital environments through a careful investigation and simulation-based analysis. Using the PRISMA approach, we choose 67 of the initial 312 papers that were related to our research. We choose these studies because they were related to our research. We created a multi-objective optimization model: f(x) = αC(x) + βE(x) + γL(x). By using this strategy, they keep our expenses, energy use, and latency low while also keeping safety and flexibility high. We were able to simulate the mixed architecture that was demonstrated in MATLAB and NS-3 by using evidence-of-stake and PBFT consensus approaches, edge computing, and ecologic routing. The results indicate that these systems have 37% less latency, 24% more energy efficiency, and 18% less carbon emissions than systems that simply use 5G. The results show that using these three in combination helps to build solid basis for Internet of Things, medical facilities, and smart towns. This, in turn, leads to the building of facilities that are not just incredibly effective but also last for a very long period. The structure also sets the stage for Industry 5.0, that will lead to greater research in areas like quantum-proof Bitcoin, artificial intelligence-based control, and sustainability made feasible by 6G infrastructure.
As soon as supply chain management is done well in online marketplaces, it will lead to better logistics, lower costs, and satisfied customers. Customers are also happier. When Blockchain technology and Federated Learning (FL) are used together, they could make things safer. demand estimates that are accurate and the protection of personal information are both important. There is a lack of trust between stakeholders, data silos, and the possibility of cyberattacks, which are some of the difficulties that are associated with the supply chain management systems of today. By utilizing these models, individuals are able to make educated estimations regarding the amount of demand that will be there in the future. For the purpose of addressing these concerns, we suggest using a technique known as Blockchain-Based Decentralized Federated Learning (BC-DFL), which is a mechanism for forecasting demand. Using the immutable ledger that blockchain provides, this technology ensures the secure transportation of data. Having the ability to see demand in real time, providing members of the supply chain with increased trust, and requiring less assistance from third-party aggregators are just some of the many benefits that come with the solution that is proposed. Based on the findings, it is clear that the BC-DFL paradigm leads in an increase in demand. With five to ten nodes with fifty thousand to one hundred thousand data, the BC-DFL architecture may be able to achieve fifty rounds of proof-of-stake consensus and AES-256 encryption. The mean absolute error was 7.6, the root mean square error was 12.9, the R2 value was 0.91, and less than 5% of the data escape.
Transaction fees play a crucial role in determining the efficiency and scalability of blockchain networks, particularly in Ethereum, where gas fees fluctuate significantly due to network congestion and competitive bidding. This study analyzes transaction fee patterns in the Ethereum blockchain and their impact on network efficiency by examining key blockchain metrics such as block density, transaction size, and transaction fee variability. The findings indicate that the mean transaction fee is 0.0342 ETH, with a median of 0.0008 ETH, demonstrating significant fee variability. The study also finds a strong positive correlation (r ≈ 0.75, p < 0.01) between transaction fees and block density, as well as a moderate correlation with transaction size (r ≈ 0.58, p < 0.01), highlighting the direct impact of network congestion on fee structures. Time series forecasting with Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models reveals cyclical trends in transaction fees, often influenced by major network activities such as NFT releases, DeFi protocol surges, and high-frequency trading. The LSTM model achieves a lower RMSE (0.09) compared to ARIMA (0.15), demonstrating its superior predictive capability for fee trends. Additionally, anomaly detection techniques identify outlier transactions with fees exceeding 2.5 ETH, often associated with front-running strategies, priority gas auctions (PGA), and inefficient smart contract executions. Despite improvements introduced by EIP-1559, the findings indicate that Ethereum’s transaction fee market remains highly volatile, with block density fluctuating between 512.0% and 3896.0%, causing extreme fee spikes during congestion periods. The presence of large transactions (maximum size: 250 bytes) further amplifies fee inefficiencies, reinforcing the need for improved scalability solutions. This study underscores the necessity of Layer-2 rollups, dynamic block size adjustments, and more adaptive fee mechanisms to enhance blockchain efficiency. Future research should explore comparative studies across blockchain networks, advanced predictive modeling techniques, and the role of miner extractable value (MEV) in transaction ordering fairness. The study’s insights provide valuable guidance for developers, users, and policymakers aiming to optimize Ethereum’s transaction fee structure and enhance overall blockchain performance.
Varshan Manish, M Gokulesh, P A Dharshan Raj, K Ragavan
The blockchain based system and method for multi-form gold tokenization by using ERC-1155 token standard focuses on a single core idea of using ERC-1155 semi fungible tokens to enable scalable and trustworthy digital gold markets that simultaneously serve institutional reserve holders and retail fractional owners. This research presents the ERC-1155 token model tailored for gold assets semantics defined conversion rules between reserve classes and fractional units and describing contract interfaces required for custody aware issuance and secure redemption and also by providing a reference implementation (smart contract + client DApp). The implementation on public Ethereum test nets is evaluated to measure operational characteristics of the ERC-1155 token standard. The study also discusses governance and custody considerations necessary to make ERC-1155 based gold tokenization practical. Results show that a single ERC-1155 contract can express both fungible fractions and non-fungible reserve classes while being computationally inexpensive and is 13.6% more relatively efficient when it comes to burning operation while it is 0.63% more relatively efficient when it comes to minting operation when compared with the same operations that makes use of ERC-721 token standard.
Ethereum, as a leading blockchain platform, experiences high variability in transaction fees due to network congestion, gas bidding, and computational complexity. This study analyzes 10,000 Ethereum transactions to identify key factors influencing transaction fees, block density, and staking mechanisms. The results show that transaction fees vary significantly, with an average of 0.1826 ETH and a standard deviation of 0.2381 ETH, indicating substantial fluctuations. A strong positive correlation (r = 0.72) between transaction size and transaction fee confirms that larger transactions incur higher costs due to increased computational demand. Time-series analysis reveals periodic spikes in gas fees, aligning with network congestion patterns. Block density averages 1718.8% (std = 501.01%), showing that some blocks are highly congested while others are underutilized. An Isolation Forest anomaly detection model identifies 3.4% of transactions as outliers, exhibiting unusually high gas fees, which may be caused by priority-based bidding, inefficient smart contract execution, or potential fee manipulation. Further analysis demonstrates that Coin Age and Stake Reward significantly influence transaction success rates. Transactions with older coins show a 7.8% higher success rate, indicating that validators may prioritize transactions with greater historical weight. Additionally, Stake Reward positively affects the Block Generation Rate (p < 0.05), confirming its role in securing the network and optimizing transaction processing. These findings provide valuable insights for Ethereum users, developers, and validators to optimize gas fees, transaction timing, and staking incentives. While this study offers critical observations, future research should focus on real-time gas fee monitoring, deep learning-based congestion forecasting, and the impact of Layer-2 scaling solutions. Understanding Ethereum’s Proof-of-Stake (PoS) dynamics will be essential for ensuring fair transaction processing, reducing gas fees, and improving blockchain efficiency.