Dodi Setiawan, Sri Sutjiningtyas, A. Eka Hermia Fitrianingsy, Ronald Naibaho · 5 authors
Blockchain consensus mechanisms are critical for ensuring security, efficiency, and scalability in decentralized networks. This study qualitatively examines ten widely used consensus algorithms—Proof of Work (PoW), Proof of Stake (PoS), Delegated PoS (DPoS), PBFT, Raft, Proof of Authority (PoA), Hybrid PoW/PoS, DAG/IOTA, Hashgraph, and Tendermint—within the research context of Bintan, Riau Islands, Indonesia. Performance was evaluated through literature review and simulated network observations, focusing on transaction throughput (TPS), latency, energy consumption, and network stability. Results indicate that DAG/IOTA and Hashgraph achieve the highest throughput with minimal latency, making them suitable for IoT and enterprise-scale applications. PoS and PoA offer energy-efficient alternatives, while PoW provides high security at the cost of high energy usage. Hybrid PoW/PoS demonstrates balanced performance across multiple metrics. Qualitative analysis highlights trade-offs among energy efficiency, throughput, latency, and decentralization. These findings provide practical guidance for selecting consensus mechanisms according to network requirements, operational constraints, and sustainability considerations, contributing a consolidated perspective on blockchain efficiency and scalability.
Dileep Kumar Murala, G. Siva Krishna, P. N. V. M. Syamala Rao, Madhusudana Rao Nalluri · 5 authors
Integrating blockchain technology with cloud computing has enabled Blockchain as a Service (BaaS), a cloud-based paradigm that allows users to design, deploy, and manage customized blockchain applications, including smart contracts and domain-specific business functionalities. BaaS providers manage infrastructure provisioning, maintenance, and scalability while ensuring quality of service (QoS) compliance through service level agreements (SLAs). However, existing resource management approaches often struggle to satisfy dynamic performance requirements in a cost-efficient manner without increasing operational overhead or reducing provider efficiency. This paper presents a structured Adaptive SLA-based assistance framework for deploying Hyperledger Fabric on cloud platforms. The framework integrates automated performance monitoring using Hyperledger Caliper, SLA violation detection through programmed SLA chaincode, and automated VM scaling via the OpenStack4J API within a 3E (effective–efficient–economical) verification methodology. A greedy multi-objective scheduling mechanism guides parameter optimization by selecting scaling actions according to marginal performance gain per unit cost. Experiments were conducted on the Nectar Research Cloud using Hyperledger Fabric 2.5 to evaluate the impact of VM size, block size, peer count, and storage configuration on throughput (TPS) and average latency. Results demonstrate that the framework consistently achieves performance targets of 50%, 100%, and 200% above the baseline configuration through adaptive resource reconfiguration. Among evaluated strategies, Comb2, which combines dual block size adjustment with VM scaling, emerged as the optimal balanced configuration in terms of performance and cost efficiency. While automated monitoring, SLA enforcement, and VM scaling are fully implemented, continuous online scheduling under dynamically changing workloads remains future work. The proposed framework establishes a practical foundation for SLA-driven blockchain optimization in cloud environments and supports future extensibility to additional BaaS platforms.
Autonomous multi-agent systems powered by Large Language Models (LLMs) are increasingly deployed in high-frequency algorithmic trading, decentralized finance (DeFi), and complex financial decision-making workflows. However, existing multi-agent interaction architectures rely heavily on implicit semantic trust: context passing between upstream and downstream agents occurs via unauthenticated, unstructured natural language or JSON payloads. This design creates critical vulnerabilities, exposing systems to indirect prompt injection, context tampering, system prompt spoofing, and multi-turn cascade poisoning. When an upstream agent ingests malicious external data, adversarial payloads can propagate through the inter-agent execution graph, bypassing single-agent perimeter guardrails and hijacking downstream financial execution logic. To resolve these vulnerabilities, we introduce AgentShield-Crypto, a zero-trust cryptographic framework for multi-agent LLM trading pipelines. AgentShield-Crypto enforces the Know-Your-Agent (KYA) protocol, replacing probabilistic natural language filters with deterministic cryptographic state boundaries. Under KYA, every agent's identity, static system prompt hash H(S_i), temporal liveness timestamp t_i, and output payload M_i are encapsulated into cryptographically signed state envelopes (E_i) using HMAC-SHA256. Inter-agent communication channels are guarded by Inline Cascading Anomaly Firewalls (ICAF), which evaluate verification predicates before allowing state transitions into downstream context windows. We construct and release AgentInject-Bench v1.0, an empirical benchmark comprising 7,000 test vectors spanning direct prompt injections, indirect context hijacking, multi-turn cascade poisoning, system prompt spoofing, and clean financial market baselines across GPT-4o, Claude 3.5 Sonnet, and Llama-3-70B-Instruct. Empirical evaluation demonstrates that AgentShield-Crypto achieves a 100.00% Defense Mitigation Rate (DMR) with a 0.00% False Positive Rate (FPR), completely eliminating multi-hop context hijacking while incurring sub-millisecond per-message execution latency (0.382 ms).
Mohammad Quthbul Widad, Rizky Parlika, Firza Prima Aditiawan
Although Bitcoin is acknowledged as the largest cryptocurrency by market capitalization and trading volume in the world's financial market, investors face a great deal of risk and uncertainty due to its exceptionally high volatility and non-linear price changes. To provide a data-driven foundation for risk reduction and forecasting support, accurate modeling techniques are crucial. This work attempts to provide a thorough comparative analysis mapping the precise accuracy–efficiency trade-off between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models under a standardized Grid Search hyperparameter optimization pipeline using a recent Bitcoin closing-price dataset spanning from January 1, 2020, to January 1, 2026. The research methodology follows a structured data science pipeline, beginning with data acquisition from Yahoo Finance, followed by preprocessing using Min-Max Scaling fitted strictly on the training partition to eliminate data leakage. Model development involves an experimental approach where both LSTM and GRU neural controllers are tuned to extract optimal structural weights. The predictive precision of these models is rigorously evaluated using three standard metrics: MAE, RMSE, and MAPE, while processing throughput is measured via hardware execution times. The research findings indicate that the optimized LSTM model achieved superior one-step-ahead predictive precision with a MAPE of 2.32%, whereas the GRU model recorded a higher error rate of 3.94%. However, the GRU model demonstrated a significant advantage in computational efficiency, completing the training process 8.45 times faster than LSTM. In conclusion, while LSTM is recommended as a forecasting support tool for high-precision financial analysis, GRU remains a viable, parameter-efficient alternative for real-time monitoring on resource-constrained systems before real-world financial deployment.
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.
Blockchain technology, initially developed as a backbone for cryptocurrencies, has rapidly expanded into broader domains of finance and business. Its unique attributes – transparency, decentralization, immutability, and enhanced security – offer solutions to persistent challenges in financial services. This chapter examines blockchain applications beyond cryptocurrency, focusing on its role in transforming financial services such as Know Your Customer (KYC), cross-border payments, and compliance. A case-based exploration of blockchain-enabled KYC demonstrates how distributed ledgers can streamline identity verification while ensuring trust and efficiency. The chapter also outlines benefits, limitations, and potential applications, contributing to a holistic understanding for policymakers, financial institutions, researchers, and practitioners.
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.
The mushrooming digitalization of industries has heightened the need to have secure and resilient system design as well as smart system design that is prone to address intricate cyber threats and data breaches. Artificial Intelligence (AI) and Blockchain have become the new influential technological innovations that could contribute greatly to the security, visibility, and reassurances of any digital ecosystem. AI can be used to provide smart threat detection and predictive analytics, intelligent decision-making, and blockchain can be employed to provide decentralization trust, immutability, and secure data sharing. The chapter discusses the prospects of AI and blockchain in the design of secure digital architectures and how the two can be used concurrently to enhance cybersecurity, keep data intact, and ensure operational resilience. It talks about architectural structures, practical implementation in industries, and ethical or regulatory implications as well as the future opportunities to create a solid and reliable digital systems in a more globalized world.
Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
In this chapter, you will learn about the privacy limitations of public blockchains such as Bitcoin and Ethereum, and how these limitations have led to the development of privacy-focused cryptocurrencies. You will study the motivations for privacy coins and the risks posed by government-issued digital currencies. The chapter introduces and compares three major privacy coins: Zcash, Dash, and Monero. For each, you will explore their underlying technologies, including zero-knowledge proofs (zk-SNARKs), CoinJoin-style mixing, and ring signatures with RingCT and stealth addresses. You will also learn about consensus protocols, supply models, and the trade-offs each project makes between privacy, usability, and scalability. Finally, you will analyze the comparative strengths and weaknesses of these systems and understand the broader implications of privacy on blockchains.
Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
This chapter introduces blockchain network structures in both UTXO- and account-based systems. It begins with Bitcoin’s transaction and address graphs, showing how Satoshi Nakamoto’s design defines network topology and enables modeling through transaction graphs, address graphs, and chainlets. Privacy coins like Monero and Zcash extend this framework with ring signatures and zero-knowledge proofs that obscure data but still permit partial inference. Ethereum shifts focus to account-based networks, covering coin and token transactions, contract interactions, and trace analysis. Ripple concludes the chapter with credit networks built on trust lines and path-based settlements, showing how global credit flows can be modeled graphically.
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。
K Venkatesh K Venkatesh, Gorre Bharath, Jannu Subhas Chandra Boss
ABSTRACT The rapid growth of the Internet of Things (IoT) has enabled billions of interconnected devices to exchange data across smart cities, healthcare systems, industrial automation platforms, and intelligent transportation networks. Despite its transformative potential, IoT environments remain highly vulnerable to cyberattacks due to limited device resources, centralized architectures, weak authentication mechanisms, and insecure communication channels. Traditional security frameworks often struggle to provide scalable trust management and tamper-resistant data protection in large-scale IoT deployments. This paper proposes a Blockchain-Based IoT Security Architecture that integrates distributed ledger technology, smart contracts, edge computing, and zero-trust authentication mechanisms to enhance security, privacy, and system reliability. The proposed framework enables decentralized device authentication, immutable transaction recording, secure data sharing, and automated access control through blockchain networks. Smart contracts dynamically enforce security policies and verify device identities before granting network access. Experimental evaluation demonstrates improvements in attack resistance, data integrity, authentication efficiency, and network trustworthiness compared with conventional centralized security approaches. The proposed architecture provides a scalable and resilient security solution for next-generation IoT ecosystems. Keywords: Blockchain, Internet of Things, Cybersecurity, Smart Contracts, Zero-Trust Architecture, Edge Computing, Distributed Ledger Technology, IoT Authentication.
Ravindran Kandasamy, Chandan Chavadi, H. Chittoo, Nidhi Shukla
Online commerce, despite its infinite development possibilities, now raises the specter of global security. A huge amount of personal data is at risk from cyberattacks, such as hacking and identity theft, that harm companies and consumers alike. The traditional way of keeping everything in one place leads to unauthorized access and manipulation, thus requiring stronger security measures. The same decentralized, unbreakable encryption and immutable record keeping that give these barter platforms strong protection against fraud are also features of distributed ledger technology. Decentralization removed control from one single source, making it less likely that there will be any tampering and deception will become slim. Blockchain networks featuring “smart contracts” that make the terms of a deal transparent and enforce contracts without the need for go-betweens. This chapter provides an analysis of how the blockchain can enhance e-privacy in e-commerce, with a focus on the foundations and attributes of blockchain to overcome current threats. As the technology becomes widespread, real cases are proving to revolutionize data security. New Use Cases And Research Using Distributed Ledgers For Enhanced Security.
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.