Alan J. McNamara, Sara Shirowzhan, Samad M.E. Sepasgozar
Purpose This study identifies and validates opportunities for automation of problematic construction contract administrative tasks and processes. Through the evaluation of identified automation opportunities, system features are proposed and prioritised to form a development roadmap for future intelligent contract (iContract) creation and evolution. Design/methodology/approach This study applies a qualitative approach to draw on experienced construction practitioners with direct knowledge of contract administration practices. Thematic mapping and co-occurrence analysis of interview data identify βContract Process Automation Opportunitiesβ (CPAOs) which are then evaluated and prioritised to inform a development roadmap. Findings The study establishes ten evaluation criteria, specific to contract processes and identifies eight novel CPAOs. Ten iContract system features, along with the technological and environment requirements to facilitate development, are then synthesised into the novel iContract development roadmap. Research limitations/implications An βiContract system requirements identification modelβ is developed by adapting established process automation theoretical frameworks. This guided the structured selection of suitable automatable contractual processes, based on both theoretical and practical insights. The roadmap offers a practical guide for iContract developers for an initial artefact and future researchers aiming to overcome the evolutionary challenges highlighted. Practical implications The roadmap offers a practical guide for iContract developers for an initial artefact and future researchers aiming to overcome the evolutionary challenges highlighted. Originality/value This study contributes a unique and founding iContract system development roadmap, in an embryonic field, that has been borne and validated by industry practitioners. It identifies the initial functions to successfully develop an iContract artefact and highlights the evolution of the concept towards an autonomous solution.
Traditional e-learning and academic administration systems face persistent challenges, including credential fraud, inefficient verification processes, and a lack of transparency in financial aid distribution. This paper proposes a blockchain-integrated e-learning platform designed to address these issues by creating a secure, transparent, and automated ecosystem for academic records and financial aid. We present a hybrid architecture that leverages a high-performance permissioned blockchain, Purechain, to manage trust-critical functions while retaining conventional databases for dynamic content. The system is built around four modular smart contracts: IdentityRegistry, AcademicManager, EduToken, and ScholarshipFactory. The key innovation is the programmatic linkage between on-chain academic performance and automated scholarship disbursement, enabling a trustless, unbiased, and efficient financial aid model.
This article is devoted to the issue of cryptocurrency seizure, using Bitcoin as an example. First, the article analyzes the legal nature of virtual currencies, cryptocurrencies, and Bitcoin, taking into account their technical aspects and their disposability. Particular attention is paid to the methods of storing cryptocurrency, which have a direct impact on the legal regulations that can be applied in the area of enforcement. Next, the possibilities of enforcing bitcoin on the basis of the applicable regulations, including the provisions on the enforcement of claims (Articles 895 to 908(1) of the Code of Civil Procedure) and other property rights (Articles 909 to 912 of the Code of Civil Procedure). Keywords: virtual currency, cryptoasset, cryptocurrency, blockchain, bitcoin, seizure, judicial enforcement, judicial enforcement proceedings, property law, virtual assets, digital assets
The aim of this dissertation is to test the applicability of two strategies β Dollar Cost Average (DCA) and Lump-Sum (LS) β in the context of the crypto market. We tested these strategies on three assets, namely Bitcoin, Ethereum and Ripple. We developed a simulation using daily historical data recorded over a period of nine years. We then calculated performance ratios and created an AR-GARCH model to analyse their properties and predictive capacity more effectively. Our empirical results show that all assets are highly volatile and exhibit heavy tails and asymmetry. Additionally, they are moderately to highly correlated with each other. We also presented proof of higher Sharpe and Sortino ratios for DCA strategies, with Bitcoin performing better than the other two assets. The results also show that Bitcoin has low-to-moderate shock sensitivity and high persistence; Ethereum has low shock sensitivity and high persistence; and Ripple has both high shock sensitivity and persistence. Furthermore, we observed the impact of strategy choice on volatility. When compared to DCA, LS lowered shock sensitivity in Bitcoin and Ripple, enhancing persistence, while having an insignificant effect on Ethereum. Finally, we demonstrate that our model exhibits superior predictive capacity with regard to Ripple compared to Bitcoin and Ethereum, and that all three assets are inefficient. These findings contribute to previous literature by providing novel empirical data and attesting to the attributes of cryptocurrencies. Furthermore, this thesis improves financial awareness and provides investors with valuable information.
Veil: Verified Encrypted Intelligence LayerA Censorship-Resistant Communication Protocol Using Blockchain-Derived Ephemeral Keys Overview The Bitcoin-Hashed Transport Protocol (BHTP) is a novel time-based obfuscation layer that renders encrypted network traffic statistically indistinguishable from random noise. By deriving ephemeral encryption keys from blockchain data, BHTP eliminates the cryptographic handshakes and traffic signatures exploited by Deep Packet Inspection (DPI) systems for protocol identification and censorship. Key Features Handshake-Free Encryption: Keys derived from publicly observable blockchain dataβno key exchange to fingerprint Traffic Indistinguishability: AES-256-GCM ciphertext with standardized padding appears as random bytes Layered Security: "Russian Doll" architecture separates transport obfuscation from payload confidentiality Automatic Key Rotation: ~10-minute (Bitcoin) or ~5-second (Stellar) key lifecycle Synchronization Tolerance: Lookback window handles propagation latency Minimal Overhead: ~0.2ms computational cost per message Versions Version Entropy Source Key Rotation Smart Contracts Status v1.1 Bitcoin ~10 min No Current v2.0 Stellar ~5 sec Soroban Specified v3.0 Hybrid Oracle ~5 sec Soroban + VRF Planned Architecture βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β BHTP Message β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ β βββββββββββββββββββββββββββββββββββββββββββββββββββββ β β β Outer Layer (Transport) β β β β AES-256-GCM + BLAKE3(Blockchain) β β β β Key Lifetime: ~10 min / ~5 sec β β β β Purpose: Censorship Resistance β β β β βββββββββββββββββββββββββββββββββββββββββββββββ β β β β β Inner Layer (Payload) β β β β β β NIP-44 / XChaCha20-Poly1305 β β β β β β Key Lifetime: Indefinite β β β β β β Purpose: Confidentiality β β β β β β βββββββββββββββββββββββββββββββββββββββββ β β β β β β β Original Message β β β β β β β βββββββββββββββββββββββββββββββββββββββββ β β β β β βββββββββββββββββββββββββββββββββββββββββββββββ β β β βββββββββββββββββββββββββββββββββββββββββββββββββββββ β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ Quick Start Key Derivation (Bitcoin) use blake3::Hasher; pub fn derive_transport_key( block_hash: &[u8; 32], prev_hash: &[u8; 32], timestamp: u64, ) -> [u8; 32] { let mut hasher = Hasher::new(); hasher.update(block_hash); hasher.update(prev_hash); hasher.update(×tamp.to_be_bytes()); *hasher.finalize().as_bytes() } Key Derivation (Stellar) pub fn derive_transport_key_stellar( ledger_sequence: u64, prev_ledger_hash: &[u8; 32], close_time: u64, vrf_output: Option<&[u8; 32]>, ) -> [u8; 32] { let mut hasher = blake3::Hasher::new(); hasher.update(&ledger_sequence.to_be_bytes()); hasher.update(prev_ledger_hash); hasher.update(&close_time.to_be_bytes()); if let Some(vrf) = vrf_output { hasher.update(vrf); } *hasher.finalize().as_bytes() } Applications Private AI Access BHTP enables invisible AI API communication: User ββ BHTP Client ββ [Random Noise] ββ BHTP Relay ββ AI Provider Access AI from censored regions Private AI usage in corporate environments No metadata about prompts or usage patterns Censorship-Resistant Messaging Standard Nostr messaging with transport obfuscation via Kind 10059 events. Event Structure { "kind": 10059, "created_at": 1702300800, "tags": [ ["h", "000000000000000000024bead8df69990852c202db0e0097c1a12ea637d7e96d"], ["e", "bitcoin"], ["p", "recipient_pubkey_hex"], ["iv", "random_nonce_hex"] ], "content": "base64_encoded_ciphertext...", "pubkey": "sender_pubkey_hex", "sig": "schnorr_signature_hex" } Security Model Property Outer Layer Inner Layer Algorithm AES-256-GCM XChaCha20-Poly1305 Key Source BLAKE3(Blockchain) ECDH (secp256k1) Key Lifetime ~10 min / ~5 sec Indefinite Provides Obfuscation Confidentiality Recoverable By Anyone (public chain) Private key holder only Hardening Roadmap Phase Features v1.1 Core protocol, Bitcoin entropy v1.2 Timing jitter, rate limiting, Noise Protocol v2.0 Stellar entropy, 5-sec rotation, Soroban v3.0 Hybrid VRF oracle, constant-rate shaping, Nym mixnet Requirements Rust [dependencies] blake3 = "1.5" aes-gcm = "0.10" bitcoin = "0.31" # For v1.1 soroban-sdk = "20.0.0" # For v2.0+ JavaScript npm install blake3 @noble/ciphers bitcoinjs-lib stellar-sdk Documentation BHTP_Specification_v1.1.md - Full protocol specification BHTP_Stellar_Specification_v2.0.md - Stellar-based specification BHTP_Soroban_Contract_Architecture.md - Smart contract details Citation @techreport{mcgirl2025bhtp, author = {McGirl, Timothy}, title = {The Bitcoin-Hashed Transport Protocol: A First-Principles Approach to Metadata-Resistant Communication}, year = {2025}, month = {December}, institution = {Independent Research}, type = {Technical Specification}, version = {1.1} } To strengthen the decoy strategy, implement an automated traffic generation module that produces fake, padded events indistinguishable from legitimate traffic1. Configure the system to support a variable decoy-to-real ratio (e.g., defaulting to 0 but allowing up to 10:1 for high-security contexts) to flood relays with noise2. Future iterations should integrate deterministic traffic shaping, where clients transmit fixed-size buckets at constant intervals (e.g., every 8 seconds), ensuring that 90% of the stream is decoy data to eliminate volume-based fingerprinting entirely. To eliminate all government and corporate spying, one must achieve a state of "Zero-Trust Sovereignty" where no data leaves your control without mathematically unbreakable encryption and total metadata obfuscation. This requires running all software on open-source, user-audited hardware (such as RISC-V) to eliminate supply-chain backdoors, and routing all network traffic through a multi-hop, mixnet-integrated transport layer (like the proposed BHTP-Stellar architecture) to render communication statistically indistinguishable from background noise. Ultimately, 100% privacy demands the complete decoupling of identity from infrastructure: using distinct, ephemeral cryptographic keys for every interaction, funding operations solely through private decentralized ledgers (e.g., Monero), and physically isolating critical endpoints in Faraday environments to prevent hardware-level signal exfiltration. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The Bitcoin-Hashed Transport Protocol A First-Principles Approach to Metadata-Resistant Communication Technical Specification v1.1 β Proposed NIP Timothy McGirl β’ Independent Researcher β’ December 2025 Abstract Modern encrypted communication protocols achieve strong content confidentiality but systematically fail to protect communication metadata. Deep Packet Inspection (DPI) systems can identify, track, and block encrypted communications without decrypting payload content. This paper presents the Bitcoin-Hashed Transport Protocol (BHTP), a novel time-based obfuscation layer that leverages the Bitcoin blockchain as a globally synchronized source of cryptographic entropy. By deriving ephemeral AES-256-GCM encryption keys from Bitcoin blockchain data using BLAKE3, BHTP eliminates the cryptographic handshakes and traffic signatures that enable DPI systems to identify and block encrypted protocols. The protocol implements a "Russian Doll" architecture: an outer transport layer providing censorship resistance through time-based obfuscation (~10-minute key rotation), and an inner payload layer (NIP-44) providing end-to-end confidentiality through XChaCha20-Poly1305. This specification includes complete cryptographic construction, formal security analysis, threat model evaluation, padding schemes for anti-fingerprinting, lookback windows for synchronization tolerance, failure mode handling, performance benchmarks (~0.2ms overhead), and reference implementation in Rust. Proposed as a Nostr Implementation Possibility (NIP) using event kind 10059. Keywords: traffic analysis, censorship resistance, metadata protection, Bitcoin, Nostr, ephemeral encryption, deep packet inspection, protocol obfuscation 1. Introduction The fundamental promise of cryptography is confidentiality: the assurance that only intended recipients can access protected information. Modern encryption algorithms fulfill this promise with remarkable effectivenessβAES-256, ChaCha20-Poly1305, and elliptic curve cryptography provide computational security guarantees that render brute-force attacks infeasible. Yet despite these achievements, encrypted communications remain systematically vulnerable to traffic analysis, a class of attacks that bypass cryptographic protections entirely by exploiting metadata: who communicates with whom, when, how frequently, and data volume exchanged. The metadata problem is not theoretical. DPI systems deployed at national firewalls identify and selectively block encrypted protocols based on traffic signatures. The Great Firewall of China, Iran's filtering infrastructure, and similar systems exploit handshake patterns, packet size distributions, timing correlations, and protocol-specific headers. Former NSA Director Michael Hayden's statement "We kill people based on metadata" accurately reflects the operational value sophisticated adversaries extract from communication patterns. 1.1 Limitations of Existing Solutions Existing approaches to metadata protectio
Vikash Singh, Little, Barrett, Phil Hayes, Fang, Max Β· 7 authors
Verifying the private liquidity state of Lightning Network (LN) channels is desirable for auditors, service providers, and network participants who need assurance of financial capacity. Current methods often lack robustness against a malicious or compromised node operator. This paper introduces a methodology for the verification of LN channel balances. The core contribution is a framework that combines Trusted Execution Environments (TEEs) with Zero-Knowledge Transport Layer Security (zkTLS) to provide strong, hardware-backed guarantees. In our proposed method, the node's balance-reporting software runs within a TEE, which generates a remote attestation quote proving the software's integrity. This attestation is then served via an Application Programming Interface (API), and zkTLS is used to prove the authenticity of its delivery. We also analyze an alternative variant where the TEE signs the report directly without zkTLS, discussing the trade-offs between transport-layer verification and direct enclave signing. We further refine this by distinguishing between "Hot Proofs" (verifiable claims via TEEs) and "Cold Proofs" (on-chain settlement), and discuss critical security considerations including hardware vulnerabilities, privacy leakage to third-party APIs, and the performance overhead of enclaved operations.
This study examines the relationship between market efficiency and digital financial innovation in the context of global financial transformation over the past decade, when fintech, cryptocurrency, and Decentralized Finance (DeFi) have significantly altered price formation and information dissemination mechanisms. The main issue raised is whether the Efficient Market Hypothesis (EMH) theory remains relevant in the face of digital market dynamics characterized by high volatility, speculative behavior, and regulatory uncertainty. The objective of this study is to assess the impact of digital innovation on information efficiency, price transparency, and the stability of modern financial markets. The study used the Systematic Literature Review (SLR) method, examining 15 scientific articles published between 2015 and 2025 from various academic databases. The findings indicate that digital technology increases access and speed of information distribution, but does not always result in consistently efficient markets. Crypto and DeFi markets have been shown to exhibit fluctuating efficiency due to price anomalies, information asymmetry, and weak regulation. Overall, the literature synthesis confirms that market efficiency in the digital era is dynamic and influenced by the interaction between technology, investor behavior, and governance quality. This study concludes that the EMH remains relevant as a basic framework, but needs reinterpretation to suit the complex and rapidly changing characteristics of digital markets.
Love Allen Chijioke Ahakonye, Hamza Ibrahim, Jae-Min Lee, DongβSeong Kim
Smart contract environments are increasingly targeted by stealthy, adaptive attacks that evade conventional rule-based or static anomaly detection systems. Inspired by the anglerfishβs bioluminescent filament, which perceives and lures activity in dark, dynamic environments, this research introduces a Bioluminescent Filament-Inspired Artificial Intelligence Perception framework for smart contract intrusion detection. The proposed model emulates biological sensory adaptation through multi-modal attention layers that dynamically illuminate anomalous behaviors in contract execution flows. By integrating self-supervised temporal perception with context-driven feedback, the framework continuously refines its detection sensitivity while maintaining low computational overhead. We evaluate the framework using fuzz-tested smart contract vulnerability datasets that simulate diverse malicious execution behaviors observed in Ethereum environments, demonstrating over 98% detection accuracy with a 40% reduction in latency compared to traditional deep learning-based IDS models. This biologically inspired perception paradigm offers a scalable, energy-efficient solution for securing blockchain-based decentralized systems against evolving threat vectors.
<p>The rapid evolution of decentralized finance (DeFi) has brought revolutionary innovations to global financial systems; however, it has also revealed some major security vulnerabilities, especially of smart contracts. Traditional auditing methods and static analysis tools are prone to fail in identifying sophisticated threats, including reentrancy attacks, front-running, oracle manipulation, and honeypots. This review discusses the growing role of machine learning (ML) in enhancing the security of DeFi systems. It provides a comprehensive overview of modern ML-based methods related to the detection of smart contract vulnerabilities, transaction-level fraud detection, and oracle trust assessment. The paper also provides publicly available datasets, necessary toolkits, and architectural designs used for developing and testing these models. Additionally, it provides future directions like federated learning, explainable AI, real-time mempool inspection, and cross-chain intelligence sharing. While it is full of promise, the application of ML in DeFi security is plagued by issues like data scarcity, interoperability, and explainability. This paper concludes by highlighting the need for standardised benchmarks, shared data initiatives, and the integration of ML into development pipelines to deliver secure, scalable, and reliable DeFi ecosystems.</p>
AI development requires reliable datasets, yet todayβs data supply chains face challenges in traceability, authenticity and ethical compliance. This study introduces a blockchain-assisted data integrity framework that ensures transparent and verifiable provenance for AI model training. The proposed system uses smart contracts to record data lineage, ownership, preprocessing transformations and annotation events. IPFS-based off-chain storage reduces blockchain load while ensuring immutability. A verification engine allows auditors to evaluate dataset compliance with ethical and regulatory standards, including bias mitigation and consent validation. Experiments utilized three real AI workflows: medical imaging, sentiment analysis and environmental sensor classification. Findings show a 92 percent reduction in provenance disputes and an improvement in audit efficiency by 41 percent. The system also provides tamper-resistant documentation supporting responsible AI governance. Latency tests show minimal performance impact due to parallelized validation nodes. This research demonstrates that blockchain can provide a robust backbone for ethical AI ecosystems, where transparency and trust are critical. Future work will explore confidentiality enhancements using zero-knowledge proofs.
The Bitcoin-Hashed Transport Protocol: A First-Principles Approach to Metadata-Resistant Communication Technical Specification v1.1 β Proposed Nostr Implementation Possibility (NIP) Overview Modern encrypted communication protocols achieve strong content confidentiality but systematically fail to protect communication metadata. Deep Packet Inspection (DPI) systems deployed at national firewalls and network chokepoints can identify, track, and selectively block encrypted communications without ever decrypting payload contentβexploiting handshake patterns, packet size distributions, timing correlations, and protocol-specific signatures. The Bitcoin-Hashed Transport Protocol (BHTP) addresses this fundamental limitation through a novel approach: deriving ephemeral transport encryption keys from the Bitcoin blockchain, a globally synchronized and publicly observable source of cryptographic entropy. By eliminating key exchange negotiations entirely, BHTP renders encrypted traffic statistically indistinguishable from random noise to any observer not synchronized with the blockchain. Technical Architecture The Russian Doll Model BHTP implements a layered security architecture providing defense in depth: Outer Layer (Transport Obfuscation): AES-256-GCM encryption with keys derived via BLAKE3 from Bitcoin block hashes. Keys rotate approximately every 10 minutes with each new block. Provides censorship resistance by defeating real-time traffic analysis. Inner Layer (Payload Confidentiality): Standard NIP-44 encryption using XChaCha20-Poly1305 with keys derived from ECDH between Nostr identity key pairs. Provides true cryptographic confidentiality independent of transport layer security. This separation reflects that censorship resistance and confidentiality are orthogonal concerns with different security requirements and threat models. Key Derivation Function Kβ = BLAKE3( Hβ β Hβββ β Tβ ) Where: Hβ: Current block hash (32 bytes) Hβββ: Previous block hash (32 bytes) Tβ: Block timestamp (8 bytes, big-endian) The 72-byte input produces a 256-bit AES key. Including both current and previous hashes prevents edge-case failures during block propagation and increases entropy. Protocol Specification Event Structure (Kind 10059) json { "kind": 10059, "created_at": <unix_timestamp>, "tags": [ ["h", "<block_hash_hex>"], ["p", "<receiver_pubkey>"], ["iv", "<aes_gcm_nonce_hex>"] ], "content": "<base64_ciphertext>", "pubkey": "<sender_pubkey>", "sig": "<schnorr_signature>" } Anti-Fingerprinting Measures Standardized Padding: ISO/IEC 7816-4 padding to bucket sizes (1 KiB, 16 KiB, 256 KiB, 1 MiB) prevents size-based traffic analysis Lookback Window: Decryption attempts against Hβ, Hβββ, Hβββ accommodate block propagation latency and minor reorganizations Timestamp Validation: Events rejected if created_at exceeds 20 minutes from referenced block timestamp Failure Mode Handling Missing block headers: Queue messages until consensus reestablished (MUST NOT fallback to cleartext) Decryption failure: Retain temporarily for potential reorganization; discard after 1 hour Inner layer failure: Discard silently (message not intended for recipient) Security Analysis Threat Model Assumes adversary with: network observation at backbone level, sophisticated DPI capabilities, active probing, historical traffic recording ("harvest now, decrypt later"), and full blockchain access. Bounded by: no endpoint compromise, no private key access, no blockchain manipulation capability. Security Properties Traffic Indistinguishability: AES-256-GCM ciphertext is computationally indistinguishable from random bytes; bucket padding eliminates size-based fingerprinting Cost Asymmetry: Legitimate users: ~0.2ms per message. Mass surveillance adversary: O(N Γ B) decryption attempts for N packets across B blocks Layer Independence: Transport layer compromise reveals only NIP-44 ciphertext; inner layer security unaffected The Permanent Record Threat: Explicitly acknowledgedβouter layer provides temporal obfuscation, not long-term secrecy. Inner NIP-44 layer provides actual confidentiality. Performance Characteristics Operation Time Throughput BLAKE3 (72 bytes) ~50 ns 1.4 GB/s AES-256-GCM (1 KB) ~150 ns 6.6 GB/s Total per message ~0.2 ms 5,000 msg/s Bandwidth overhead: ~3x for small messages (dominated by padding), decreasing proportionally for larger payloads. Implementation Bitcoin Header Acquisition Options: Full node (most trustworthy, ~500 GB storage) SPV client (~50 MB headers with proof-of-work validation) Multi-API queries (lightweight, trust assumptions) Library Requirements: Rust: blake3, aes-gcm, bitcoin crates JavaScript: blake3, @noble/ciphers, bitcoinjs-lib Python: blake3, cryptography, python-bitcoinlib Reference implementation provided in Rust demonstrating complete encryption/decryption flow. Contributions Complete cryptographic construction for time-based transport obfuscation using Bitcoin block hashes Layered "Russian Doll" security architecture separating censorship resistance from confidentiality Full protocol specification with data structures, procedures, padding, and failure handling Formal security analysis with proofs for indistinguishability, cost asymmetry, and layer independence Performance benchmarks and implementation guidance Keywords traffic analysis, censorship resistance, metadata protection, Bitcoin, Nostr, ephemeral encryption, deep packet inspection, protocol obfuscation, BLAKE3, AES-256-GCM, NIP-44, decentralized communication
Raghavan Sheeja, Sherwin Richard R., Shreenidhi Kovai Sivabalan, Srinivas Madhavan
<p>The generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isnβt any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device.</p>
The complexity class Quantum Statistical Zero-Knowledge ($\mathsf{QSZK}$), introduced by Watrous (FOCS 2002) and later refined in Watrous (SICOMP, 2009), has the best known upper bound $\mathsf{QIP(2)} \cap \text{co-}\mathsf{QIP(2)}$, which was simplified following the inclusion $\mathsf{QIP(2)} \subseteq \mathsf{PSPACE}$ established in Jain, Upadhyay, and Watrous (FOCS 2009). Here, $\mathsf{QIP(2)}$ denotes the class of promise problems that admit two-message quantum interactive proof systems in which the honest prover is typically computationally unbounded, and $\text{co-}\mathsf{QIP(2)}$ denotes the complement of $\mathsf{QIP(2)}$. We slightly improve this upper bound to $\mathsf{QIP(2)} \cap \text{co-}\mathsf{QIP(2)}$ with a quantum linear-space honest prover. Specifically, the honest prover uses space linear in the size of the transcript of the original $\mathsf{QSZK}$ proof system. A similar improvement also applies to the upper bound for the non-interactive variant $\mathsf{NIQSZK}$. Our main techniques are algorithmic versions of the Holevo-Helstrom measurement and the Uhlmann transform, both implementable in quantum linear space, implying polynomial-time complexity in the state dimension, using the recent space-efficient quantum singular value transformation of Le Gall, Liu, and Wang (CC, to appear).
Tan GΓΌrpinar, Mehmet Akif Gulum, Melanie Martinelli
Enterprises today face increasing threats from cyberattacks, supply chain disruptions, and systemic market risks, making the enhancement of organizational resilience through advanced risk management frameworks increasingly critical. Traditional approaches often struggle to balance data privacy, cross-organizational collaboration, and real-time adaptability. While distributed ledger technologies (DLTs) initially enabled cryptocurrencies, they have evolved into a foundational infrastructure for decentralized AI applications. This study investigates how decentralized AI techniques, particularly federated learning, can support joint risk management processes in enterprise networks. First, a comprehensive review of decentralized AI methods is conducted to identify approaches suitable for enterprise risk management. Next, expert interviews are used to contextualize these insights, highlighting practical considerations, organizational challenges, and adoption constraints. Building on the literature and expert feedback, a decentralized framework is developed to allow organizations to securely share risk-related insights while preserving data privacy and control over proprietary information. The framework is validated through a technical prototype, combining architectural design with empirical proof-of-concept experiments on federated learning benchmarks. Results demonstrate the feasibility of achieving near-centralized model accuracy under privacy constraints, while also highlighting communication and governance issues that need to be addressed in real-world deployments. The study presents a structured comparison of decentralized AI techniques and a validated concept for enhancing supply chain risk prediction, fraud detection, and operational continuity across enterprise networks.
This article analyzes the prospects and limitations of implementing blockchain technologies in the insurance industry, with a particular focus on the Russian market. The relevance of the study is driven by the sector's conservatism, rising fraud, pressure from digitalization, and demand for transparency. Despite blockchain's potential, its widespread adoption faces barriers: regulatory uncertainty, high costs, and mistrust among market participants. Therefore, the authors identify and categorize the technological, regulatory, and organizational limitations to the large-scale use of distributed ledgers in insurance. Particular attention is paid to assessing the prospects for adapting blockchain technologies to the Russian insurance market, taking into account its specific characteristics.
In the decentralized Internet environment, growing awareness of user data sovereignty has raised higher requirements for privacy protection in blockchain scenarios. To enhance the security and controllability of data authorization, this study develops a model integrating zero-knowledge proof (ZKP), field disclosure control, and multi-party joint verification. The ZKP ensures verifiable privacy, field disclosure control minimizes data exposure, and multi-party verification strengthens consistency and tamper resistance. Through this collaborative integration, the model forms a unified framework for secure and transparent data authorization. Experimental results on two blockchain datasets show that the model outperforms comparison approaches in authorization accuracy, field matching consistency, and verification efficiency, achieving a minimum verification loss of 0.248 and a true positive rate of 96.8%. Under simulation conditions, it maintains stable performance across different complexity levels, with authorization accuracy of 95.1% and field validation consistency of 96.5%. Compared with traditional single-mechanism methods, the model delivers comprehensive improvements in privacy strength, verification transparency, and collaborative trust, demonstrating strong potential for application in high-sensitivity blockchain privacy protection scenarios, particularly in privacy-critical domains such as healthcare record management, financial data exchange, and supply chain traceability.
This contribution will focus on geographical / geopolitical changes over time as depicted in maps on stamps. A traditional definition of a stamp would probably be βusually a rectangular piece of paper of varying colour and denomination, affixed to a letter etc. to cover the cost of postageβ. Today, however, the word rectangular could easily be replaced by triangular, round or even map shaped. The paper could be cloth or even chocolate. Stamps even exist as crypto or non-fungible tokens, in other words a stamp with a digital twin. All these changes over time are new revenue models for postal authorities, as very few people use stamps to pay for postage. Stamps, in whatever form, offer a small window into a nation's society, nature and culture. They aim to give a country a profile by depicting its people, identity and territory. People and identity are often linked to heritage. This leads to themes of monarchs and political leaders, flags and heraldry, traditional costumes, folklore, etc. However, when looking at the timeline of a country's stamp issues, the timeframe provides a context for the choices made regarding the themes. In the case of territory, the nation or area is represented and identified by locational and boundary features. These could be typical landscape features and maps. The first stamp issues of a 'new' nation often include a map claiming the territory and a flag to emphasise identity. Figure 1a shows an example from the Faroe Islands. In such situations, stamps can become geopolitical tools. This can be harmless, as in Figure 1a, where the outline of a country is depicted. However, sometimes nations use the map on the stamp to claim part of the territory of neighbouring countries. It is interesting for cartographers to look at the design of these carto-philatelic items. The maps depicted are not always designed with the medium, the small piece of paper, in mind. The maps could be reduced details of existing maps or they could be designed specifically for the stamp. In many cases, however, the rules of cartographic design are not necessarily followed. Figure 1b shows the outline of France on a stamp from Equatorial Guinea. In cases such as this, external organisations create stamp series for a postal authority on subjects that do not offer a window into a nation's society but have commercial objectives. There are also more sophisticated stamp designs (Figure 1c). Liechtenstein's First Day Cover shows a map of land use, which on the stamp is transformed into a schematic diagram symbolising land use. The cancellation stamp is also a map. Time series of map stamps exist in many forms. The most common is a series of historic maps of a region, showing the evolving knowledge of the shape of the area as new surveying techniques became available. A series of four topographic map details was issued to commemorate the 200th anniversary of the Ordnance Survey (Figure 1d). Canada issued a series of four stamps showing the expansion of the country over time (Figure 1e). Comparing different map stamp issues of an area over time can show how the perspectives of the authorities have changed, introducing geopolitics in the time series. Several examples are shown in the figure. Figure 1f shows Panama, first as part of Colombia, as an independent nation, with a gap because of the Panama Canal Zone and the situation when the Canal Zone was returned to Panama. Pakistan, shortly after independence, showed the territory of Kashmir and Jammu as disputed, but more recently is seen as an integral part of the country (Figure 1g). Figure 9 shows Suriname. Its extent in the south-east and south-west overlaps with the claims of French Guiana and Guyana respectively. However, at the 15th year of their independence, the Surinam map inadvertently omits these claims, which are reinstated in later postage stamp editions. Sometimes the time series of stamps are issued to cover up mistakes. The stamps of Guernsey (Figure 1h) are such an example, which issued a map with an incorrect latitude, placing the island near Madrid. Other anomalies will be discussed in this paper with a focus on the influence of map design.
Even in this day and age, when digital technologies are becoming more and more prevalent, it is still extremely important for democratic systems to maintain the honesty and openness of their voting procedures. This article introduces NextGenVote, a decentralised online voting platform developed to address the security, transparency, and confidence issues traditional electronic voting systems face. Automation of election operations, including voter registration, candidate administration, ballot casting, and result computation, is achieved through smart contracts written in the Solidity programming language. The system is built on the Ethereum blockchain. MetaMask is a React-based frontend that uses Web3.js to connect to the blockchain. MetaMask is responsible for ensuring that user authentication and transaction signatures are secure. Therefore, to prevent unauthorised manipulation, the platform utilises a role-based access control approach that clearly distinguishes between administrative capabilities and voter credentials. NextGenVote assures that election results are tamper-proof, traceable, and auditable. It was deployed and tested in a local blockchain environment powered by Ganache. The system provides a solid foundation for scalable, secure, and transparent digital elections by eliminating centralised intermediaries and relying solely on processes executed on the blockchain.
Abstract This essay argues that social media document (rather than fuel) the decline of political democracy while helping revive organizational democracy, including through βdecentralized autonomous organizationsβ (DAOs). Yet, despite giving everyone a voice and the ability to organize across borders, social media could overβconcentrate power if, in the future, a few large but siloed platforms ended up shrinking viewpoint diversity β the oxygen of democracy. How can we curb corporate platform concentration without dulling democracy? Due to tradeβoffs in platform design, no single service can deliver free speech, free usage, and safe usage simultaneously. Fortunately, this βtrilemmaβ can be transcended at the industry level with an interoperability mandate that fosters user multihoming and lets various platforms provide different bundles of democratic benefits. Email works across service providers, and so can social media. Interoperability thus represents a viable answer based on six advantages: practical feasibility; competition on merit; faster complementor innovation; jurisdictional flexibility; unlocking network effects between, rather than just within platforms; and alignment with democratic values. Platform interoperability can make social media social again and futureβproof democracy. This proposal is a clarion call for blaming the Internet a little less for democracyβs problems and instead leveraging its infrastructure strategically to address them.
We model the ultimate price paid by users of a decentralized ledger as resulting from a two-stage game where Miners (/Proposers/etc.) first purchase blockspace via a Tullock contest, and then price that space to users. When analyzing our distributed ledger model, we find: - A characterization of all possible pure equilibria (although pure equilibria are not guaranteed to exist). - A natural sufficient condition, implied by Regularity (a la [Mye81]), for existence of a ''market-clearing'' pure equilibrium where Miners choose to sell all space allocated by the Distributed Ledger Protocol, and that this equilibrium is unique. - The market share of the largest miner is the relevant ''measure of decentralization'' to determine whether a market-clearing pure equilibrium exists. - Block rewards do not impact users' prices at equilibrium, when pure equilibria exist. But, higher block rewards can cause pure equilibria to exist. We also discuss aspects of our model and how they relate to blockchains deployed in practice. For example, only ''patient'' users (who are happy for their transactions to enter the blockchain under any miner) would enjoy the conclusions highlighted by our model, whereas ''impatient'' users (who are interested only for their transaction to be included in the very next block) still face monopoly pricing.
Digital certificate forgery remains a real problem in education and employment because traditional verification processes rely on centralized databases, are vulnerable to manipulation, and often take a long time. This study designs and implements a blockchain-based digital certificate verification system that models certificates as Non-Fungible Tokens (NFTs) using the ERC-1155 standard on the Manta Pacific Layer 2 network and incorporates a Soulbound Token (SBT) mechanism to ensure that certificates cannot be transferred. The research adopts a prototyping method through eight stages, starting from architecture design and prototype development to the integration of ERC-1155 smart contracts with IPFS and wallets, as well as testing of minting functions, QR code-based verification, and rejection of asset transfers. The results demonstrate successful on-chain certificate issuance with significantly reduced transaction costs compared to ERC-721-based certificates on Layer 1 networks reported in previous studies, while maintaining a decentralized audit trail. The SBT implementation successfully rejects every attempt to transfer certificates to other wallets, thereby preventing the sale or illicit transfer of credential ownership. These findings indicate that the combination of ERC-1155, SBT, and IPFS on a Layer 2 network has strong potential as an efficient, secure, and practically adoptable digital certificate verification model for educational institutions.
Affective artificial intelligence has made substantial advances in recent years; yet two critical issues persist, particularly in sensitive applications. First, these systems frequently operate as 'black boxes', leaving their decision-making processes opaque. Second, audit logs often lack reliability, as the entity operating the system may alter them. In this work, we introduce the concept of Immutable Explainability, an architecture designed to address both challenges simultaneously. Our approach combines an interpretable inference engine - implemented through fuzzy logic to produce a transparent trace of each decision - with a cryptographic anchoring mechanism that records this trace on a blockchain, ensuring that it is tamper-evident and independently verifiable. To validate the approach, we implemented a heuristic pipeline integrating lexical and prosodic analysis within an explicit Mamdani-type multimodal fusion engine. Each inference generates an auditable record that is subsequently anchored on a public blockchain (Sepolia Testnet). We evaluated the system using the Spanish MEACorpus 2023, employing both the original corpus transcriptions and those generated by Whisper. The results show that our fuzzy-fusion approach outperforms baseline methods (linear and unimodal fusion). Beyond these quantitative outcomes, our primary objective is to establish a foundation for affective AI systems that offer transparent explanations, trustworthy audit trails, and greater user control over personal data.
Damilare Peter Oyinloye, Mohd Sameen Chishti, Jingyue Li
Single-bridge blockchain solutions enable cross-chain communication. However, they are associated with centralization and single-point-of-failure risks. This paper proposes Proof of Success and Reward Distribution (PSCRD), a novel multi-bridge response coordination and incentive distribution protocol designed to address the challenges. PSCRD introduces a fair reward distribution system that equitably distributes the transfer fee among participating bridges, incentivizing honest behavior and sustained commitment. The purpose is to encourage bridge participation for higher decentralization and lower single-point-of-failure risks. The mathematical analysis and simulation results validate the effectiveness of PSCRD using two key metrics: the Gini index, which demonstrates a progressive improvement in the fairness of the reward distribution as new bridge groups joined the network; and the Nakamoto coefficient, which shows a significant improvement in decentralization over time. These findings highlight that PSCRD provides a more resilient and secure cross-chain bridge system without substantially increasing user costs.
N Mangala, Murtaza Rangwala, S Aishwarya, B Eswara Reddy Β· 8 authors
Healthcare has become exceptionally sophisticated, as wearables and connected medical devices revolutionize remote patient monitoring, emergency response, medication management, diagnosis, and predictive and prescriptive analytics. Internet of Things and Cloud computing integrated systems (IoT-Cloud) facilitate sensing, automation, and processing for these healthcare applications. While real-time response is crucial for alleviating patient emergencies, protecting patient privacy is paramount in data-driven healthcare. In this paper, we propose a multi-layer IoT, Edge, and Cloud architecture to enhance emergency healthcare response times by distributing tasks based on response criticality and data permanence requirements. We ensure patient privacy through a Differential Privacy framework applied across several machine learning models: K-means, Logistic Regression, Random Forest, and Naive Bayes. We establish a comprehensive threat model identifying three adversary classes and evaluate Laplace, Gaussian, and hybrid noise mechanisms across varying privacy budgets, with supervised algorithms achieving up to 83.6% accuracy. The proposed hybrid Laplace-Gaussian noise mechanism with adaptive budget allocation provides a balanced approach, offering moderate tails and better privacy-utility trade-offs for both low and high-dimension datasets. At the practical threshold of $\varepsilon$=5.0, supervised algorithms achieve 80-81% accuracy while reducing attribute inference attacks by up to 18% and data reconstruction correlation by 70%. We further enhance security through Blockchain integration, which ensures trusted communication through time-stamping, traceability, and immutability for analytics applications. Edge computing demonstrates 8$\times$ latency reduction for emergency scenarios, validating the hierarchical architecture for time-critical operations.