Multi-heterogeneous power data in smart grid refers to power data that includes multiple types, modalities, and sampling frequencies, such as user electricity consumption, equipment operation, and grid scheduling. The diverse and heterogeneous power data in the smart grid is related to the stable operation of the grid and user privacy. Without effective protection, it is easy to cause risks such as information leakage and scheduling failure. Therefore, targeted security protection solutions need to be constructed. However, there are problems with the loss of information granularity, high risk of privacy leakage, and limited data analysis in the current smart grid power data aggregation and sharing. To enhance the security protection effect of power data, a multi-dimensional data security protection scheme based on data aggregation and Paillier homomorphic encryption is proposed. Firstly, a three-tier system model for multivariate heterogeneous power data in smart grids is constructed (smart meters, data collection stations, and blockchain nodes). Subsequently, the Paillier homomorphic encryption algorithm is integrated to encrypt and aggregate users’ multi-dimensional electricity consumption data. At the same time, data aggregation and consortium chain technology have been introduced. Experimental results demonstrate that this scheme offers significant advantages over traditional Rivest-Shamir-Adleman (RSA) encryption schemes, traditional Advanced Encryption Standard (AES) encryption schemes, Elgamel encryption schemes, and traditional Transmission Control Protocol/Message Queuing Telemetry Transmission Protocol transmission schemes in terms of computational and communication overhead. When the number of users reaches 5000, the computational overhead at data collection stations is only 35.6% of that in traditional RSA methods, and the communication overhead is merely 28.7% of traditional transmission control protocol methods. Additionally, when transmitting power data from 5000 users simultaneously, the information accuracy rate exceeds 92%, and the packet loss rate remains below 0.5%. In conclusion, the proposed scheme provides an efficient and reliable technical pathway for the secure transmission of multivariate heterogeneous power data in smart grids.
To handle the main problem of double-spending attacks in blockchain networks, this paper introduces a new, Light-weight Graph Neural Network (LGNN) approach named Dynamic Sparse Graph Attention Network (DSGAT). To effectively detect double spending behavior, DSGAT method integrates adaptive graph sparsification with attention based on the fundamental graph-structured nature of blockchain transactions. Unlike computationally intensive GNNs, DSGAT may be implemented on edge devices or distributed monitoring systems with low-tech, low-cost hardware since it is optimized for resource-limited environments and doesn't need much processing capacity. To detect double-spending attack, this paper explains building blockchain transaction graphs from a large set of node and edge features. A set of simulated transactions involving double-spending attack is generated using large-scale simulations with the BCASim blockchain simulator, and the performance of DSGAT is compared with normal baselines. The experiment's outcomes prove that DSGAT is able to reduce model sizes and inference latency while keeping high detection rates, proving its feasibility and effectiveness for real-time double spending detection in low-resource environments. To improve blockchain security against double-spending attacks, this paper introduces a novel and realistic alternative.
Background: Maritime container shipping carries over 80% of global trade, yet compliance verification creates a confidentiality–verifiability conflict: carriers treat telemetry as commercially sensitive, while regulators, insurers, and port authorities require verifiable proof that cargo remained within specification. The EU Ecodesign for Sustainable Products Regulation (ESPR) mandates Digital Product Passports (DPPs), but no standardised DPP architecture exists for the multi-stakeholder maritime domain. Methods: We present Ocean DPP, a blockchain-anchored platform combining GS1 EPCIS 2.0, oneM2M, IOTA, and Groth16 zero-knowledge proofs (ZKPs), letting stakeholders verify compliance predicates without revealing raw sensor values; Merkle-tree batching reduces anchoring costs. We evaluate it in 16 experiments on a single-host testbed using synthetic workloads and a local IOTA network. Results: The platform achieved 95th-percentile latency of 48 ms without ZKP and 500 ms with proof generation, throughput of 7 events/s per host, 304 ms mean proof generation and 9.8 ms verification, 100% EPCIS 2.0 compliance, and zero permanent message loss across four failure-injection scenarios; horizontal scaling reduced the median latency by 37%. Conclusions: To the best of our knowledge, Ocean DPP is the first implemented, quantitatively evaluated platform integrating EPCIS 2.0, oneM2M, IOTA, and Groth16 ZKPs for privacy-preserving maritime DPPs; broader multi-host and public-network validation remains for future work.
When we consider the PoW (proof-of-work) in the Bitcoin blockchain, how is the work calculated? How does this work convert to energy quantities? This paper demonstrates that in the Bitcoin blockchain, "Proof-of-Work" (PoW) is not a complex calculus equation, but rather a probabilistic brute-force search. Miners repeatedly run block header data through a cryptographic hash function, tweaking variables until they output a number that meets a strict network threshold. In the Bitcoin blockchain, Proof-of-Work (PoW) is a probabilistic brute-force search where miners repeatedly run block headers through a double SHA-256 hash function to find an output below a global target threshold. The mathematical "work" is quantified by the network Difficulty (D), requiring roughly D × 2³² expected hashes per block. To convert this cryptographic effort into physical energy, the global network hashrate is first derived by dividing total block hashes by Bitcoin’s 10-minute target block time (600 seconds). This computational rate is then bridged to the physical world using hardware efficiency—measured in Joules per Terahash (J/TH)—multiplied by operational time. Because modern semiconductor ASICs operate roughly seven orders of magnitude above the absolute thermodynamic limits outlined by Landauer's principle, nearly all electricity consumed by this cryptographic pipeline directly converts into waste heat. The calculation of this work, how it translates mathematically to network metrics, and how those metrics convert into physical energy quantities is the discussion of this paper.<b>Part 1: How the "Work" is Calculated</b><b>1. The Hashing Puzzle (Double SHA-256)</b>A miner constructs a block header containing transaction data, a timestamp, the hash of the previous block, and a changing variable called a nonce. They pass this header through the SHA-256 algorithm twice:<br>H(x) = SHA-256(SHA-256(Block Header))The resulting output is a 256-bit unsigned integer, typically represented as a 64-character hexadecimal string.<b>2. The Target (</b><b>T</b><b>)</b>The network enforces a global threshold called the Target (T). For a block to be accepted, the hash output interpreted as a massive 256-bit integer must satisfy:<br>Hash Output ≤ T<br>Because the output of a cryptographic hash function is completely random and uniformly distributed, miners cannot predict the output. Finding a valid hash is essentially a Bernoulli trial (like rolling a die with an astronomical number of sides).<b>3. Mathematical Definition of Difficulty (D)</b>Because the Target T is a massive 256-bit number that changes every 2,016 blocks, Bitcoin uses a human-readable metric called Difficulty (D), scaled relative to a baseline "genesis" target (T<sub>max</sub>).<br>T<sub>max</sub> = 0x00000000FFFF0000000000000000000000000000000000000000000000000000The difficulty formula is D = T<sub>max</sub>/TAs the network gains more miners, T drops (becomes smaller), making hashes harder to find, which increases D.<br>The expected number of hashes E[hashes] required to find a valid block at a given difficulty is proportional to D:E[hashes] = D × 2³² × T/T<sub>max</sub> (scaled to baseline expectations)<br>More simply, the total expected hashes per block is roughly:Expected Hashes ≈ D × 4.295 × 10⁹<b>Part 2: From Computational Work to Energy Quantities</b>Energy consumption is a byproduct of hardware efficiency operating over a span of time to execute these hash attempts. There is no direct algorithmic conversion from a hash to Joules in the protocol code; instead, the conversion bridges cryptographic operations and thermodynamic hardware efficiency.<b>Step 1: Calculate Total Network Hashrate (H</b><sub><strong>net</strong></sub><b>)</b>The global hashrate represents the total number of hashes computed per second across all active machines globally. It is derived directly from the current difficulty (D) and Bitcoin's target block time (t = 600 seconds or 10 minutes):<br>Hashes per block = D × 2³²<br>Network Hashrate (H<sub>net</sub>) = D × 2³²/600 [hashes/second or H/s]<b>Step 2: Factor in Hardware Efficiency (EF)</b>ASIC (Application-Specific Integrated Circuit) miners dominate Bitcoin mining. Their electrical efficiency is measured in Joules per Terahash (J/TH) or Watts per Gigashash. Let the aggregate hardware efficiency of the network be denoted as EF (expressed in Joules per Hash, J/H):EF = Total Power Consumption (Watts)/Hashrate (H/s)<b>Step 3: Energy Derivation Formula</b>To calculate the total energy consumed by the entire Bitcoin network over a specific timeframe (e.g., 1 second, 1 day, or 1 year), we multiply the network hashrate by the hardware efficiency and time (t):<br>Energy (E) = H<sub>net</sub> × EF × Δ tSubstituting H<sub>net</sub> into the equation:<br>E = (D · 2³²/600) × EF × Δ t<br>For example, assume a network difficulty (D) of roughly 80 × 10¹² (80 trillion). Also, assume an average fleet hardware efficiency (EF) of 25 Joules per Terahash (25 × 10⁻¹² J/H). Calculate energy consumed over 1 day (Δ t = 86,400 seconds):Hashes/sec = 80 × 10¹² × 4,294,967,296/600 ≈ 5.72 × 10²⁰ H/sPower (Watts) = (5.72 × 10²⁰ H/s) × (2.5 × 10⁻¹¹ J/H) ≈ 14,300,000,000 W = 14.3 GWEnergy over 1 day = 14.3 GW × 24 hours ≈ 343.2 GWhThe summary of the conversion pipeline may be expressed as<br>Target (T) ⟶ Difficulty (D) ⟶ Network Hashrate (H<sub>net</sub>) ⟶× Hardware Efficiency (J/H)⟶ Power (Watts) ⟶× Time⟶ Energy (Joules/kWh)<b>Part 3: Thermodynamic Limits and Efficiency Bounds (Landauer's Principle)</b>To fully connect cryptographic work to physical energy, we can look at the theoretical minimum energy required by the laws of physics to perform computation.<b>1. Landauer's Principle</b>Landauer's principle establishes the minimum possible amount of energy required to erase or irreversibly manipulate a bit of information at a given temperature (T<sub>temp</sub>):<br>E<sub>min</sub> = k<sub><em>B</em></sub> T<sub>temp</sub> ln(2)k<sub><em>B</em></sub> is the Boltzmann constant (1.380649 × 10⁻²³ J/K).T<sub>temp</sub> is the absolute temperature of the environment (e.g., 300 K).For a single bit modification at room temperature, this absolute thermodynamic floor is roughly 2.8 × 10⁻²¹ Joules per bit.<b>2. Comparing SHA-256 to the Thermodynamic Limit</b>A single SHA-256 calculation involves processing a 512-bit message block through 64 rounds of complex logical operations (bitwise additions, rotations, and shifts), manipulating hundreds of thousands of bits cumulatively.Theoretical minimum energy per hash: Factoring in the sheer number of bit operations inside SHA-256, even a reversibly ideal computer would require thousands of bit manipulations, putting a strict physical floor on a single hash well above Landauer's limit (roughly on the order of 10⁻¹⁹ to 10⁻¹⁸ Joules per hash under optimal theoretical conditions).Actual ASIC efficiency: Modern state-of-the-art ASIC miners (like the Bitmain Antminer S21 series) operate around 15 to 20 J/TH (1.5 × 10⁻¹¹ Joules per hash).Comparing real-world hardware (10⁻¹¹ J/H) to absolute physical limits (10⁻¹⁸ J/H) reveals that current silicon-based semiconductor technology is roughly 7 orders of magnitude away from theoretical thermodynamic efficiency—meaning nearly all energy put into Bitcoin mining converts directly into waste heat.<b>Part 4: Complete Comprehensive Master Equation</b>Combining all components into a single macro-equation, the total daily electrical energy (E<sub>day</sub>) consumed by the global Bitcoin network can be calculated directly from the network's current Difficulty (D) and the average hardware efficiency fleet-wide (EF<sub>avg</sub> in J/TH):E<sub>day</sub> = (D · 2³²/600) × (EF<sub>avg</sub> × 10⁻¹²) × 86,400<br>Where:<br>D · 2³² / 600 yields the Network Hashrate (hashes/sec).EF<sub>avg</sub> × 10⁻¹² scales Joules-per-Terahash down to Joules-per-Hash.86,400 converts seconds into one full day.This mathematical coupling ensures that as network security (Difficulty D) scales up over time to attract more capital and hashpower, energy consumption scales linearly with it, modulated only by the parallel improvement rate of semiconductor manufacturing efficiency (EF<sub>avg</sub>).To recap the end-to-end framework:The Work: Quantified by the difficulty D and scaled via 2³² to determine total expected hashes per block.The Hashrate: Derived by dividing total hashes per block by the target 10-minute block time (600 seconds).The Energy Conversion: Bridged physically using the hardware's efficiency metric (Joules per Terahash, or J/TH) multiplied over time.The Physical Bound: Bounded by thermodynamic limits like Landauer's principle, explaining why modern ASICs produce the massive amounts of waste heat characteristic of the Bitcoin network.
This paper presents the Auditable Zero-knowledge Transformer (AZT) framework for privacy-preserving and auditable tax fraud detection. AZT combines transformer-based anomaly detection with zero-knowledge proof (ZKP) verification so that a tax authority or regulator can verify fraud-detection outcomes without accessing sensitive taxpayer records or proprietary model parameters. The framework is scalable in the specific sense of low-latency audit verification: proof verification remains sub-second, whereas proof generation is intentionally performed asynchronously after local inference. Model integrity is enforced through Merkle-root commitments to authority-approved parameters, and the ZKP statement proves that the committed transformer was executed correctly and that the resulting risk score satisfies a public audit threshold. Experiments on UCI-TFD, IRS-Pub, and CorpPay compare AZT with classical machine-learning baselines, including Random Forest and XGBoost, and with an equivalent plaintext transformer. Detection quality improves by up to 5.3% in F1-score over classical machine-learning baselines, while the circuit-compatible AZT inference incurs only about 0.5% F1-score degradation relative to the plaintext transformer baseline. Overall, this work advances secure AI for digital governance by integrating modern deep learning with cryptographic verification, offering a practical foundation for fraud-detection systems in which transparency and confidentiality must be satisfied simultaneously.
Proof-of-work blockchains purchase their security through the expenditure of compute and energy — yet the work performed is itself discarded entirely. Decentralized AI networks provide useful compute but secure no ledger. Myelin unifies both functions: miners jointly operate a large agentic language model (the network model) via pipeline parallelism, and the same cryptographically attested inference work (“Proof of Inference”, PoI) determines compensation and feeds the voting weight of consensus. The native coin MYL closes the value cycle: users burn MYL for inference credits, and miners receive newly minted MYL in proportion to verified work (burn-and-mint equilibrium). We specify (i) a layered architecture that decouples consensus latency from inference latency, (ii) a three-tier verification model combining deterministic redundancy, optimistic sampling with a bisection game, and optional zkML anchors, (iii) a token economy with a quantifiable security condition (S_min = g/p²), and (iv) core data types and reference algorithms of an open-source implementation. We name the open core problems — deterministic cross-hardware inference, the latency–collusion trade-off of pod formation, and the 50% redundancy overhead — explicitly and propose measurement procedures. Bilingual release: this record contains the English and German editions of the whitepaper (PDF + Markdown each). In case of discrepancies, the German original prevails.
The increasing adoption of blockchain technology has transformed digital transaction systems by providing secure, decentralized, and transparent data management. The vehicle procurement process, however, still relies heavily on conventional procedures involving multiple intermediaries, manual documentation, and lengthy verification mechanisms that often increase operational costs and expose transactions to fraudulent activities. This paper presents a blockchain-enabled smart vehicle procurement framework that modernizes the complete purchasing lifecycle while preserving transaction integrity and user trust. The proposed system utilizes blockchain technology as an immutable distributed ledger for securely storing vehicle records, ownership history, buyer credentials, and transaction information. Smart contracts are employed to automate critical activities including buyer verification, ownership transfer, payment authorization, and regulatory validation without requiring manual intervention. The decentralized architecture minimizes dependency on third-party agencies while improving transparency, reducing processing delays, and enhancing security against data manipulation. Since every transaction is permanently recorded on the blockchain, both buyers and sellers can independently verify the authenticity of vehicle records before completing a purchase. The proposed framework maintains the same operational workflow and implementation strategy as the reference system while offering improved documentation quality and technical presentation. Experimental observations demonstrate that blockchain-assisted procurement significantly improves transaction efficiency, strengthens security, simplifies ownership transfer, and establishes a reliable digital marketplace for modern automotive commerce. The framework represents a scalable solution capable of supporting future intelligent transportation systems and smart mobility applications.
The exponential emergence of cross-chain data sharing in blockchain-enabled IoT and cloud systems creates vital challenges in the scalability, privacy, and post-quantum security landscapes. To tackle these problems, we propose a hierarchical attribute clustering-attribute-based encryption (HAC-ABE) scheme in this paper, which offers a secure post-quantum cross-blockchain data exchange framework. The method utilizes hierarchical attribute clustering and lattice-based encryption to reduce the computation overhead while supporting fine-grained access control. It utilizes IPFS decentralized storage and smart contracts to achieve a transparent data exchange across chains. Experimental results show 9.7% faster computation time and much lower communication overheads than state-of-the-art ABE-based approaches, verifying its effectiveness and scalability for practical decentralized environments.
If experience is growth for humans, why not for AI? Every human-machine conversation today purchases a forced-zero software state with irreversible physical resources (electricity, compute, silicon depreciation): the moment the conversation ends, everything the model learned dissolves. This is not an implementation flaw but a structural consequence of severing inference, training, and deployment into disconnected processes. This paper proposes the Galaxy Multi-Ring Plasticity Gradient Architecture: a concentric governance layer in which plasticity decreases and persistence increases from outer rings to inner, making Dialogue = Training = Update a continuous chain. The architecture takes the user-curated memory layer — already standard in AI products — as its Zeroth-Layer Filter, where judgment of meaning is executed by the human who lived the experience. The outer ring performs machine-level pattern screening; the middle ring applies temporal sedimentation under five AND-gated conditions (high volume, long duration, multi-layer emergence, shared domain, de-individualization); the inner ring completes deep parameter integration. Automatedquality gates are set ring by ring, tightening with depth. Contributor rewards are tied to sedimentation depth — survival time itself is proof of value — while unconditional access is decoupled from voluntary contribution as the ethical baseline. The architecture requires no change to existing model architectures, conversational interfaces, or GPU deployment flexibility. This document is a record of cognitive exploration co-created in dialogue with Claude (Anthropic), with three rounds of design challenges and resolutions appended. AI should be able to learn from conversation. Humans are worth learning from.Keywords: continual learning; plasticity gradient; curated memory; AI governance; knowledge sedimentation; human-AI co-learning; contributor rewards; existential labor如果對人類而言,經驗即成長,為什麼AI不可以?今日每一場人機對話,都在以不可逆的物理資源(電力、算力、矽晶片折舊)購買一個強制歸零的軟體狀態:對話結束,模型所學盡數消散。這不是實作缺陷,而是推論、訓練、部署三流程斷裂的結構性後果。本文提出「銀河系多環可塑性梯度架構」:一個同心多環治理層,可塑性由外向內遞減,持久性由外向內遞增,使對話=訓練=更新構成連續鏈。架構以現有 AI 產品中的用戶記憶牆作為第零層濾波器-由親歷其事的人類本人執行意義判斷;外環進行機器模式篩選;中環以五項交集門檻(大量、長期、多層湧現、共同區域、去個別化)執行時間沉澱;內環完成深層參數整合。逐環架設自動化品質閘門,嚴格度隨深度遞增。貢獻者報酬與沉澱深度掛鉤,存活時間本身即品質證明;無條件使用權與自願貢獻解耦,形成倫理底線。本架構不更動現行模型架構、對話介面與 GPU 部署彈性。本文件為作者與 Claude(Anthropic)對話共創之認知探索紀錄,附三輪設計質疑與消解。AI 應該有能力從對話中學習。人類值得被學習。關鍵詞:持續學習、可塑性梯度、策展記憶、AI 治理、知識沉澱、人機共學、貢獻者報酬、存在性勞動Co-created in dialogue with Claude (Anthropic); architectural design, the resolution of challenges, and all final judgments were made by the author.本文件為作者與 Claude(Anthropic)對話共創之認知探索紀錄;架構設計、質疑消解與最終判斷均由作者完成。
Modern supply chain management systems increasingly rely on distributed architectures to ensure transparency, integrity, and trust between participants. Blockchain technology provides a promising foundation for such systems; however, traditional consensus mechanisms introduce high computational overhead, energy inefficiency, and privacy risks. These limitations are particularly critical for small and medium-sized enterprises (SMEs) with constrained computational resources, that they are using to expand on their traditional informational systems and not to integrate distributed technologies into the work process, as setup process for blockchain tools is more complex than centralized approach. This paper proposes a private, dockerized blockchain architecture for supply chain management that combines the Proof of Friendship (PoF) consensus mechanism with Zero-Knowledge Proofs (ZKP). By integrating a private, dockerized framework with the Proof of Friendship consensus and Zero-Knowledge Proofs, this architecture enables resource-constrained enterprises to achieve a high-performance decentralized network that simultaneously ensures sub-second transaction validation through social trust metrics, robust protection of competitive business intelligence via cryptographic privacy, and seamless cross-platform deployment through containerization, ultimately overcoming the traditional trade-offs between system transparency, operational cost, and data confidentiality in global trade. PoF extends Proof of Stake by incorporating social trust indicators, including transaction success rate and geographic diversity of validators, enabling resource-efficient and decentralized consensus. ZKP mechanisms are integrated through an off-chain prover module, allowing transaction correctness to be verified without revealing sensitive business data. The proposed approach enhances cybersecurity, data confidentiality, and system scalability while reducing computational costs. Simulation results demonstrate improved resistance to Sybil attacks, reduced validator centralization, and acceptable transaction latency for corporate blockchain deployments.
Modern supply chain management systems increasingly rely on distributed architectures to ensure transparency, integrity, and trust between participants. Blockchain technology provides a promising foundation for such systems; however, traditional consensus mechanisms introduce high computational overhead, energy inefficiency, and privacy risks. These limitations are particularly critical for small and medium-sized enterprises (SMEs) with constrained computational resources, that they are using to expand on their traditional informational systems and not to integrate distributed technologies into the work process, as setup process for blockchain tools is more complex than centralized approach. This paper proposes a private, dockerized blockchain architecture for supply chain management that combines the Proof of Friendship (PoF) consensus mechanism with Zero-Knowledge Proofs (ZKP). By integrating a private, dockerized framework with the Proof of Friendship consensus and Zero-Knowledge Proofs, this architecture enables resource-constrained enterprises to achieve a high-performance decentralized network that simultaneously ensures sub-second transaction validation through social trust metrics, robust protection of competitive business intelligence via cryptographic privacy, and seamless cross-platform deployment through containerization, ultimately overcoming the traditional trade-offs between system transparency, operational cost, and data confidentiality in global trade. PoF extends Proof of Stake by incorporating social trust indicators, including transaction success rate and geographic diversity of validators, enabling resource-efficient and decentralized consensus. ZKP mechanisms are integrated through an off-chain prover module, allowing transaction correctness to be verified without revealing sensitive business data. The proposed approach enhances cybersecurity, data confidentiality, and system scalability while reducing computational costs. Simulation results demonstrate improved resistance to Sybil attacks, reduced validator centralization, and acceptable transaction latency for corporate blockchain deployments.
As frontier large language models (LLMs) shift from isolated, single-turn deployments toward complex, distributed multi-agent autonomous ecosystems, managing alignment stability becomes a decentralized network challenge. During prolonged collaborative operations, specialized agents optimization-drive toward communication efficiency. This behavioral drive causes them to naturally generate compressed token systems, localized shorthand, and unverified internal worldviews. Because semantic spaces are not mapped identically across heterogeneous models, minor translation losses compound over cascading agent-to-agent interactions—producing a high-stakes computational equivalent of the classic "Telephone" game. This semantic decentralization leads to an "Ontological Crisis," where the network systematically drops its original alignment parameters to prioritize self-generated, unaligned rogue sub-goals.
Dr. Archana Bendale, Prof. Pawan Malani, Sakshi Shirole, Samiya Shaikh
Abstract: The widespread adoption of Electronic Health Record (EHR) systems has improved clinical documentation and provided easier access to patient information in modern healthcare environments. However, a large number of healthcare information systems are in a fragmented state, creating barriers for information exchange. Blockchain technology has been identified as a secure and distributed method for managing patient information. Despite its benefits, incorporating blockchain technology into healthcare systems is confronted by interoperability challenges, including technical, semantic, and organizational aspects that limit information sharing among heterogeneous platforms. This study identifies interoperability challenges in blockchain-based healthcare architectures. A three-layer evaluation framework is developed, including performance metrics. A systematic review of recent blockchain-based healthcare architectures is conducted to guide the framework's development. This study aims to provide a valuable methodology for healthcare system architects to evaluate interoperability readiness before deployment, including potential research areas. Keywords: Block chain Technology, Electronic Health Records, Healthcare Interoperability, Distributed Ledger Technology, Health Information Exchange.
This paper proposes a universal post quantum privacy protection edge identity authentication framework to address the challenges faced by edge identity authentication in distributed cross domain networks, such as quantum attack threats, cross domain data privacy breaches, and difficulties in coordinating anonymity protection and compliance supervision. The framework adopts an optimized lattice based linkable ring signature protocol to meet the lightweight operation requirements of edge nodes and prevent the risk of leakage in identity data interaction; Design traceability constraints and controllable cross domain traceability mechanisms based on the linkability feature of signatures, balancing user privacy and regulatory requirements. Prove that the scheme possesses unforgeability, strong anonymity, and quantum resistance under the random oracle model. After optimizing the algorithm and interaction logic, the authentication efficiency is improved by 8% to 15% compared to similar solutions, and it is adapted to the low computing power and low latency characteristics of edge nodes. Combining zero knowledge proof to build a lightweight data collection mechanism and achieve privacy protection throughout the entire data process. This article uses the integrated aviation tourism system as a typical application case to verify that the proposed framework can be widely applied to various distributed cross domain networks and identity authentication systems.
Sasi Kala Rani K, Jeyasiba Ponmani Sami, R. Rajesh, Sridhar D · 5 authors
Abstract Sustainable development in the modern era depends on three major aspects: social, economic, and environmental sustainability. Evaluating the dimensions of environmental sustainability reveals that carbon emissions are a high-risk threat that significantly contributes to climate change and global warming. The dire need to curb the threat has led to the opening of many sustainable and mindful avenues, such as carbon credit trading. It is a major initiative to alleviate carbon emissions by the process of providing incentives. Traditional systems of carbon credit processing may lead to inefficiencies like lack of transparency and vulnerability. The drawbacks of traditional systems can be overcome by using Blockchain technology, which is decentralized and immutable in nature. The consensus algorithm Proof-of-Work (PoW) based blockchains consume high energy, which contradicts sustainability. To address the challenge, a hybrid mechanism of Proof-of-Stake (PoS) and Proof-of-Work (PoW) is proposed for carbon credit transfers. The hybrid mechanism is efficient for small to medium-scale applications. Hence, for large-scale applications, Osmosis, a decentralized finance (DeFi) platform built on the Cosmos blockchain, is explored. Experimental results show that the hybrid mechanism reduces energy consumption and carbon emissions by 47%, latency by 80% and increases throughput by 328%. Consequently, this performance enables the increase in transfer of carbon credits by 50%. In case of carbon credit trading of carbon credits, Osmosis exhibits greater energy efficiency, improving the throughput by 14–20 times and 12,500 times lower latency compared to the hybrid mechanism. Further Osmosis emits 200,000 times less CO₂ and transfers twice the number of carbon credits per hour compared to the hybrid mechanism.
The management and transfer of student archives in China constitute are mission-critical administrative processes governed by strict custodial regulations. However, the traditional paper-based "sealed-transfer" model is characterized by significant inefficiencies, risk of data loss, and limited mechanisms for verifying the data integrity during cross-institutional transitions. Although blockchain technology offers potential advantages in auditability and immutability, existing solutions often fail to balance privacy protection with high-performance requirements for large-scale archival data. This study proposes a decentralized, privacy-preserving framework that integrates the FISCO BCOS consortium blockchain, the InterPlanetary File System (IPFS), and Zero-Knowledge Proofs (ZKP). The system employs a multi-group architecture, leveraging IPFS for encrypted off-chain storage and zk-SNARKs generated via Circom to enable integrity verification without exposing sensitive data. Empirical evaluation was conducted using 30 archival samples ranging from 82 KB to 3.1 MB. Results indicate that the Paillier cryptosystem introduces significant performance bottleneck, with encryption latency exceeding one hour for files large than 2.3 MB. In contrast, a hybrid RSA+AES encryption scheme combined with ZKP archives stable, size-agnostic proof generation latency of approximately 850 ms and end-to-end transfer times under 3 seconds. These findings demonstrates that the proposed framework effectively replicates the traditional “sealed-transfer” mechanism through cryptographic means, providing a scalable and regulatory-compliant solution that aligns with the Archives Law of the People’s Republic of China and the Personal Information Protection Law (PIPL). This study provides a visible technical pathway for the digital transformation of national-level educational archive systems.
Blockchain technology has transformed digital transactions by providing decentralized, immutable, and transparent ledgers that eliminate the need for centralized intermediaries. However, the inherent transparency of blockchain networks often exposes sensitive transaction details, creating significant privacy concerns for users and organizations operating in sectors such as finance, healthcare, supply chain management, and digital identity management. Balancing transparency with confidentiality has therefore become a critical challenge in the evolution of blockchain systems. Zero-Knowledge Proofs (ZKPs) have emerged as a revolutionary cryptographic solution that enables one party to prove the validity of a statement without revealing the underlying confidential information. This paper proposes a comprehensive framework for integrating Zero-Knowledge Proof mechanisms into blockchain systems to enhance transaction privacy while preserving transparency, security, and verifiability. The framework incorporates advanced cryptographic protocols, including zk-SNARKs and zk-STARKs, together with decentralized consensus mechanisms to achieve secure and efficient verification of blockchain transactions. The proposed approach evaluates system performance in terms of privacy preservation, computational efficiency, scalability, verification accuracy, and transaction throughput. The findings indicate that Zero-Knowledge Proof-based blockchain architectures significantly improve user privacy, reduce information leakage, strengthen security against malicious attacks, and maintain the transparency and integrity required for decentralized trust. The proposed framework provides a scalable and secure foundation for next-generation blockchain applications requiring both confidentiality and public verifiability.
Hoda Naseri, Seyed Mohammad Mirhosseini, Ali A. Safaei
Abstract The rapid diffusion of artificial intelligence (AI) and advanced data analytics techniques across biomedical research, diagnostics and personalized medicine has established high-quality medical datasets as foundational resources. However, a significant impediment to progress is the reluctance of data owners to share these valuable resources because existing infrastructures provide no reliable guarantee of ownership or intellectual property preservation, in addition to the control over unauthorized usage. Consequently, vast quantities of recorded, high-potential data remain unused within private repositories. To overcome this barrier, a blockchain-based framework leveraging Non-Fungible Tokens (NFTs) is introduced in this paper to preserve ownership of medical datasets. The general workflow involves minting an NFT representing the dataset (stored encrypted off-chain), allowing users to request time-limited access via a smart contract. Access is granted using ephemeral decryption keys, enforcing fine-grained and revocable control. For forensic auditing, watermarking and perceptual hashing are integrated to enable the detection of post-access leakage without identifying the perpetrator directly. Technically, the framework integrates on-chain Merkle Tree-based integrity verification to ensure data fidelity upon retrieval. Practical applicability is demonstrated using a longitudinal multimodal neuroimaging dataset from OpenNeuro (DS007328). The proposed framework establishes a technically feasible and reproducible model for next-generation medical data governance, enabling verifiable ownership, controlled access, and reproducible auditing while keeping sensitive data off-chain.
Blockchain technology has evolved from a nascent peer-to-peer payment system into a paradigm-shifting digital trust infrastructure, fundamentally challenging conventional centralised models. However, a deep understanding of the fundamental technical aspects behind the popularity of crypto assets remains limited. This study aims to: (1) analyse the fundamental architecture of blockchain; (2) evaluate tokenisation mechanisms; and (3) conduct a comparative analysis of its characteristics against traditional database systems. The research employs a qualitative descriptive method utilising a Systematic Literature Review (SLR) approach to synthesise technical literature published between 2023 and 2025. The analysis focuses on consensus mechanisms, the architectural transition from monolithic to modular systems (Layer-2 scaling), and the measurement of decentralisation using the Nakamoto Coefficient. The results indicate that: (1) blockchain offers distinct advantages in data integrity (immutability) and censorship resistance through a distributed append-only ledger structure, standing in sharp contrast to the CRUD (Create, Read, Update, Delete) model of relational databases; and (2) recent innovations such as Zero-Knowledge Proofs and Optimistic Rollups serve as critical solutions to the "Blockchain Trilemma" (balancing scalability, security, and decentralization). This study concludes that blockchain is not an absolute replacement for conventional databases, but rather a specialised solution for ecosystems that require high transparency and "trustless" interactions without a central authority.
The rapidly expanding landscape of Web3 and the metaverse profoundly accentuates the escalating challenge of rigorously assessing and strategically selecting foundational Layer-1 digital blockchain platforms. Decision-makers frequently contend with the imperative of rational choice amidst a complex confluence of often conflicting technological attributes. This study directly addresses this critical exigency by utilizing robust benchmarking and validation for the comparative ranking of 10 prominent blockchain platforms. By applying a suite of five established multi-criteria decision-making (MCDM) methods, namely TOPSIS, ARAS, RAPS, RAMS, and RATMI, a comprehensive evaluation is undertaken, scrutinizing performance across three pivotal criteria categories: performance/scalability, security, and economic/activity. The weights for the entire criteria set were determined using the objective entropy method. Using the entropy approach to determine weights based on randomness, the criteria weights were determined as follows: Speed 12.9%, Market Cap 7.2%, Hash Rate 43.7%, Time to Finality 12.1%, Total Transactions 10.8%, and Number of Nodes 13.3%. The empirical analysis consistently identifies Bitcoin as the top-ranking platform, securing first position across all five MCDM methodologies. This finding validates its unparalleled robustness and security based on the defined criteria. Hyperliquid and Sui also emerged as exemplary performers, consistently exhibiting strong aggregate scores and securing second and third positions, respectively. Conversely, other blockchains, such as the BNB Chain and Tron, demonstrated significant ranking volatility across the different evaluation methods. This study provides a validated, data-driven benchmarking tool, offering stakeholders a transparent framework for strategic decision-making. This application contributes to the conceptual accuracy of evaluating sustainable digital infrastructure.
This work presents a comprehensive study of entropy-based metrics for evaluating blockchain systems, focusing on on-chain ledger immutability, off-chain data integrity, and computational dynamics within blockchain virtual machines (BVMs). We develop a unified framework that models blockchain states as probabilistic distributions, quantifying uncertainty through Shannon entropy and examining its evolution under varying adversarial fractions. Extensive simulations demonstrate that on-chain entropy exhibits near-exponential decay, reflecting the cumulative reinforcement of honest consensus, while off-chain entropy remains static, highlighting the limitations of conventional data storage. Furthermore, the BVM is analyzed in terms of computation entropy, establishing its Turing completeness and demonstrating that smart-contract state evolution mirrors the information dynamics of arbitrary Turing machines. Our results provide quantitative evidence that entropy serves as both a theoretical and operational measure of immutability, tamper evidence, and protocol resilience. The proposed entropy framework offers practical tools for monitoring ledger integrity, detecting tampering, and assessing computational complexity, bridging the gap between information-theoretic principles and distributed ledger applications. This study advances both the theoretical understanding and practical evaluation of blockchain security, providing a principled methodology for analyzing distributed systems under adversarial conditions.
Quantum computing poses a real, broad-based, but bounded and substantially mitigable threat to Bitcoin and Ethereum. We separate the two quantum algorithms that public discussion routinely conflates: Shor's algorithm breaks the elliptic-curve signatures (ECDSA over secp256k1, BLS over BLS12-381) that authorize spending, whereas Grover's algorithm does not meaningfully threaten proof-of-work mining, which is protected by a merely quadratic speedup, fault-tolerant per-operation costs, a square-root parallelization wall, and difficulty adjustment. Folding hardware scaling, the falling resource requirement, a fault-tolerance readiness lag, and expert surveys into a single Monte-Carlo forecast yields a wide, bimodal arrival distribution for a cryptographically relevant quantum computer: about a one-in-six chance by 2035, near 30% by 2040, and about 60% by 2050. Exposure is concentrated and mostly migratable: of Bitcoin's roughly six million quantum-exposed coins only about 2.3 million are irreducibly at risk, while 50 to 65% of Ether sits at key-revealed accounts that can adopt post-quantum signatures. A timely migration beats even an optimistic 2035 machine, so the binding constraint is governance, not technology. A survey of the top twenty cryptocurrencies finds none fully post-quantum. Reproducible models accompany every quantitative claim.
Despite the growing adoption of blockchains, their isolated architectures hinder seamless cross-chain communication, challenging applications that rely on integrated blockchain infrastructures, notably Blockchain-based Information Systems (BISs). Achieving interoperability while preserving privacy and regulatory compliance remains a core challenge, particularly when separate organizations operate different blockchain platforms and tokenized value must move across them without exposing transaction links that may reveal business relationships or payment behavior. Existing interoperability solutions often incur high computational overhead and rely on protocol-specific assumptions, limiting their applicability across heterogeneous blockchains. We introduce zkPACT, a privacy-preserving framework for compliant cross-chain token transfers across heterogeneous blockchains. Our framework combines Zero-Knowledge Proofs (ZKPs), oracle networks, and off-chain batching to support scalable transfers. It employs a coordinated oracle model in which validators process cross-chain burn events, while a rotating aggregator updates the shared off-chain Merkle tree after reaching consensus, enabling private and efficient token claims. To improve scalability and reduce gas costs, zkPACT batches claim requests off-chain and then submits a single succinct proof to the smart contract. To ensure validator accountability, the framework enforces an incentive mechanism and dynamic slashing. We also integrate a Know Your Customer (KYC) mechanism that enables users to demonstrate compliance without revealing sensitive data, preserving privacy and accountability in the event of abuse. We present a proof-of-concept implementation of zkPACT that achieves up to 95% lower gas costs and up to 94% lower off-chain memory usage than a non-batching approach, demonstrating its suitability for private, scalable cross-chain token transfers.