Digital identity remains structurally rigid when it is bound to provider accounts, mutable handles, single social contexts, and local wallet schemes. This paper defines an accountable hash-anchor tier above wallets and credential schemes. Building on a companion model of legal identity assurance, the tier binds an inert root anchor to an event-backed Entity Actor Identity (EAID) assurance state, supports unlinkable profile anchors for distinct contexts, and lets relying parties evaluate gate-specific assurance-at-time over disclosed independent confirmation-event clusters. The formal model states the identifier and capability separation invariant, defines root and profile anchors, models disclosure and lawful resolution as constraints, and gives a two-layer erasure construction for retained commitments and off-chain personal linkage. It also models trust as a reliance event in which pseudonymous interaction becomes rational when a presentation satisfies the requested gate and recourse can reach the legally relevant imputation point. The result is a scheme-agnostic aggregation layer that complements national wallets, supports natural, juridical, and machine actor constellations, and states conditions under which pseudonymity, lawful access, data erasure, and retention can be jointly satisfied.
Permissioned ledgers are commonly treated as centralised because admission is restricted. This paper separates permissioning from control distribution and proposes identity-staked consensus as a trust model for accountable settlement ledgers operated by chartered validators. The model treats validator identity as externally costly collateral: public legal identity, charter state, institutional reputation, liability, attributable audit exposure, a phase-indexed conditional identity-loss floor, and loss-realisation channels outside the protocol. It distinguishes this construct from proof-of-authority by formalising validator acts as actor constellations, public validator anchoring, affiliation-aware voting caps, threshold class coverage, per-member collusion margins, observer-supported detectability, bootstrap claim discipline, and a consensus/application enforcement boundary. The paper connects the model to a broader identity-infrastructure series: the actor-assurance paper supplies capability-gate evidence, the trust-anchor paper supplies public validator anchoring and assurance-at-time, and the delegated-authority paper can consume the ledger evidence record for mandate and model-attribution records.
Despite the promise of decentralization, measurement studies have identified a conspicuous lack of decentralization in blockchains. Centralization has been observed in almost all layers of the blockchain, in decentralized applications, and in decentralized autonomous organizations. In many cases, it is practically impossible to definitively determine the extent of centralization in the system. While multiple works have proposed methods to decrease centralization, by and large blockchains continue to be significantly centralized. In this paper, we develop a general framework for building verifiably decentralized blockchain systems. Our framework is motivated by the core observation that the richness and diversity of collaborative interactions between users -- rather than resource uniformity -- captures the essence and extent of decentralization in a blockchain system. Existing blockchains do not have any incentive mechanisms to encourage inter-coalition collaboration, which directly contributes to centralization. We propose a novel reward design that incentivizes users to collaborate with other users without forming isolated coalitions. Technically, our method uses a Sybil-resistant asymmetric Shapley value for reward attribution within a collaboration group, and the theory of expander graphs for measuring and enforcing decentralization. Our framework is general and can be adapted to alleviate centralization in any layer, application, or decentralized organization. It also has important implications beyond the topic of centralization. For example, we show that our solution can naturally address the blockchain scalability problem. We also identify a new class of decentralized collaborative applications that have hitherto been unexplored in blockchains.
Transaction processing systems underpin modern commerce, finance, critical infrastructure, and emerging Self-Sovereign Digital Identity (SSDI) protocols, yet their security has never been studied holistically across the full evolutionary arc of these systems. Over five decades, transaction processing has progressed through four distinct generations, from centralized databases, to distributed databases, to blockchain and distributed ledger technologies (DLTs), and most recently to multi-context systems that span cyber-physical components under real-time constraints. Each generation has introduced new transaction types and, with them, new classes of vulnerabilities; successful exploits now cause billions of dollars in annual losses. Despite this, security research remains fragmented by domain, and the foundational ACID transaction model has not been revisited to reflect the demands of contemporary systems.
Sepideh Avizheh, Reihaneh Safavi-Naini, Shiwei Sun
Group signatures are privacy preserving signature schemes in which a group member can anonymously sign messages on behalf of the group, while providing accountability, by allowing the signature of a misbehaving group member be ``opened'' and the identity of the signer be revealed. In group signature members are admitted to the group by a (trusted) group manager. We motivate the need for a flexible mechanism in applications, such as privacy preserving access in smart environments, and propose a two-level member-join group signature that we call SPonsored Group Signature (SPGS) where group members of level 1 can ``sponsor'' new members, in level 2, to join the group. This relaxation of user join comes with additional accountability mechanisms: we require that the signature of a sponsored member can be opened to the identity of the sponsor (that is sponsor is responsible for the sponsored member), and while all signatures are anonymous, for the sponsored members, the signatures are linkable. This allows a sponsor to efficiently identify an undesirable sponsored member. We formalize SPGS scheme, define its security using a game-based approach, and give a generic construction of SPGS that uses a (dynamic) group signature scheme, a commitment scheme, and a knowledge-sound non-interactive zero knowledge proof of knowledge, and prove its security. We also give an instantiation of our construction. To show applicability of SPGS in practice, we consider the problem of providing guest access in a smart building, and introduce Anonymous Guest Access Token (AGAT) that allows a temporary guest to anonymously access (a subset of) the building resources. We show how SPGS can be used (together with an IND-CPA secure public key encryption scheme) to give a direct construction for AGAT, and show the efficiency of our guest access protocol when it is instantiated with existing schemes.
As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy? The ERC-8004 protocol addresses this challenge with the first permissionless trust layer for AI agent economies, built around three on-chain registries for Identity, Reputation, and Validation. Despite its rapid adoption, the protocol has not been studied empirically, leaving it unclear whether the information it records provides a trustworthy basis for decision-making. To address this gap, we present the first empirical study of ERC-8004 across three chains: Ethereum, BNB Smart Chain (BSC), and Base, covering the period from protocol deployment through May 13, 2026. We crawl on-chain Identity and Reputation events, off-chain files, and x402 payment transactions. On the identity side, we find that most registrations are placeholders rather than active agents, with only a small fraction (3%, 4%, and 15% across Ethereum, BSC, and Base) exposing a valid ERC-8004 registration file with at least one live service endpoint. On the reputation side, we show that the Registry, as currently deployed, cannot function as a trust signal: values are not commensurable, feedback records are rarely grounded in verifiable interactions, and reputation can be manipulated at minimal cost. Consistent with these design weaknesses, we find that a substantial fraction of reviewers (73.5%, 59.2%, and 90.6% across Ethereum, BSC, and Base) exhibit coordinated Sybil behavior. After removing Sybil-flagged feedback, 15.8%, 77.9%, and 86.8% of rated agents, respectively, are left with no valid feedback. We then turn these findings into concrete recommendations for future revisions of ERC-8004. Our study yields actionable protocol-design implications and establishes an empirical baseline for research on AI agent markets.
Pham Van Huong, Nguyen Ngoc Tuyen, D. H. Long, Trần Quốc Toanh · 5 authors
The paper proposes a comprehensive data security model for blockchain-based web applications. This model can be used as a general template for Web3 applications. The model consists of two parts: a blockchain core with integrated database encryption modules, replacing Fabric CA; and an application part that also integrates file encryption, database encryption, and digital signatures. The proposed model was tested on a VBCC management website using Hyperledger Fabric. File and database encryption uses AES, and digital signatures use ECDSA. To improve performance, we also replaced the GolevelDB database management system with LevelDB. Experimental results confirm the accuracy and good performance.
KENOS — Kohenoor Operating System Official Description and Public Disclosure KENOS, the Kohenoor Operating System, is the unified digital operating environment of the Kohenoor ecosystem. It brings together artificial intelligence, Education 3.0, blockchain infrastructure, hybrid finance, business applications, development tools, governance controls and operational supervision within one coordinated ecosystem. <Explainer film added> The transition from KENHYFI Hub to the broader KENOS architecture reflects the continued expansion of the Kohenoor ecosystem. KENHYFI was originally developed as a hybrid-finance and ecosystem hub. However, the name and positioning of KENHYFI did not fully represent the wider capabilities that had developed around it, particularly: KAI — Kohenoor Artificial Intelligence, the ecosystem’s multilayered intelligence powerhouse and orchestration system. ProEdge, the Education 3.0, professional learning and workforce-development hub. Blockchain, development, commerce, security, governance and institutional-support applications extending beyond hybrid finance. For this reason, KENOS was established as the umbrella operating environment for the complete ecosystem. KENHYFI remains an important integrated hub within KENOS, but it no longer represents the entire ecosystem by itself. The relationship is therefore defined as follows: KENOS is the complete Kohenoor Operating System and umbrella ecosystem. KAI is the principal intelligence and orchestration powerhouse of KENOS. ProEdge is the principal Education 3.0 and professional-learning hub. KENHYFI is the integrated hybrid-finance and ecosystem-services hub within KENOS. Other applications and modules provide specialized capabilities in blockchain, commerce, development, security, finance and operational management. KENOS is built on three foundational pillars: Education 3.0 Artificial Intelligence Blockchain These pillars support the complete digital-economic journey: Learn → Plan → Build → Execute → Analyze → Supervise → Improve → Scale Artificial Intelligence Pillar KAI, Kohenoor Artificial Intelligence, serves as the principal intelligence powerhouse of KENOS. KAI is designed as a multilayered hybrid-intelligence and workflow-orchestration system rather than a conventional chatbot. It supports knowledge retrieval, document analysis, specialist-role activation, business intelligence, financial analysis, educational guidance, application planning, risk assessment, reporting, workflow coordination and Human-in-the-Loop escalation. Within KENOS, KAI connects users, knowledge, applications, workflows and authorized human decision-makers. Education 3.0 Pillar ProEdge serves as the principal learning and professional-development hub within KENOS. It supports practical education, workforce transformation, professional training, institutional capacity building and industry-linked learning in areas including: Artificial intelligence Blockchain and Web3 Business intelligence Cybersecurity Hybrid finance Digital transformation Communication and professional skills Software and application development Entrepreneurship and business execution ProEdge ensures that KENOS is not limited to providing technology. It also develops the human capability required to understand, manage and apply that technology effectively. Blockchain Pillar The blockchain pillar provides smart contracts, programmable assets, digital ownership, transparent records, settlement mechanisms, token utilities and verifiable ecosystem operations. Blockchain functions are designed to operate alongside KAI-supported intelligence, business rules, governance controls and authorized human supervision. Purpose of KENOS KENOS is designed to support individuals, professionals, businesses, educational institutions, developers, government entities and other organizations participating in the AI-powered digital economy. It connects learning with intelligence, intelligence with execution and execution with monitoring and supervision. KENOS may support: Education and professional development Artificial intelligence and business intelligence Financial and hybrid-finance services Blockchain and smart-contract development Digital commerce and procurement Application and software development Security and operational resilience Governance and institutional intelligence Reporting, monitoring and supervision Development Status At the time of this publication: KENOS is in the Early Beta phase. KENHYFI Hub is in the Alpha+ phase. Individual applications and modules may have different levels of development, testing and availability. The official public web host and disclosure gateway for KENOS is: https://www.kohenoor.net Within the KENOS architecture: KAI serves as the principal intelligence and orchestration layer. KENHYFI Hub operates as an integrated hybrid-finance and ecosystem services hub. Education 3.0 platforms support learning, reskilling and professional development. Blockchain applications provide smart-contract, digital-asset, settlement and verification capabilities. Business and development modules support planning, commerce, procurement, software development, financial intelligence, security, reporting and operational management. KENOS is intended to serve individuals, professionals, businesses, educational institutions, developers, government organizations and other entities participating in the AI-powered digital economy. The architecture is modular and may support public web access, controlled organizational deployments, private-cloud environments, local installations, sovereign infrastructure and integration with existing enterprise systems. Governance remains a core element of KENOS. High-stakes activities are intended to remain subject to authorized human review, role-based permissions, validation controls, risk classification, activity logging and Human-in-the-Loop approval. At the time of this publication, KENOS is in the Early Beta phase, while KENHYFI Hub is in the Alpha+ phase. Applications and modules within the ecosystem may therefore have different levels of development, testing, availability and production readiness. The official public web host and disclosure gateway for KENOS is: https://www.kohenoor.net This publication provides the official conceptual definition, ecosystem positioning, service scope, architectural relationships, development status, governance principles and public-disclosure framework of KENOS. Keywords: KENOS; Kohenoor Operating System; Kohenoor Technologies; KAI; Kohenoor Artificial Intelligence; KENHYFI; Education 3.0; artificial intelligence; blockchain; hybrid finance; digital economy; business intelligence; digital transformation; smart contracts; Human-in-the-Loop; ecosystem architecture; AI governance; Web3; enterprise AI; institutional intelligence Kohenoor Technologies remains committed to transparency, security, responsible disclosure, and continuous improvement of the KEN ecosystem. #kenhyfi #kai #hyfi #kohenoortechnologies #futureofeducation #futureoffinance #futureofai #kohenoorken #cryptocurrencies #kohenoorken #AI #actionai #agenticai #AGI #ArtificialGeneralIntelligenceAGI #AIAssistant #education3 #defi #hybridfinance #hyfi #cedefi #blockchain #innovation #settlements #auditreadycertificates #DASC #cybersecurity #web3 #businessintelligence #proedge #industrygradetrainings #quantumcomputing
This paper presents a comprehensive analysis of the World Wide Web Consortium (W3C) decentralized identity standards and their relationship to the MF+SO sovereign identity vault architecture. We examine the W3C Decentralized Identifier (DID) Core specification (W3C, 2022), the Verifiable Credential (VC) Data Model (W3C, 2022), and related standards including DID Resolution, DID URL dereferencing, and the Verifiable Credential Proof Formats. The paper provides a taxonomic analysis of DID methods (did:key, did:ethr, did:ion, did:web, did:indy) in terms of their trust assumptions, ledger requirements, latency, cost, and privacy properties. We compare MF+SO's identity model—which uses Ed25519 public keys as self-certifying identifiers with a local hash chain for state verification—against the W3C DID Core model, identifying both alignments and divergences. Key findings include: MF+SO identifiers are functionally equivalent to DIDs but use a simplified resolution mechanism that does not require a distributed ledger or external registry; MF+SO's hash chain audit trail provides state verification properties comparable to DID Document versioning on a ledger; and MF+SO's selective disclosure mechanisms using zero-knowledge proofs (see Paper VII) directly implement the W3C Verifiable Credential selective disclosure and data minimization requirements. We analyze the interoperability implications of MF+SO's architecture, demonstrating how MF+SO DIDs can be registered on external DID methods for cross-system interoperability while maintaining the local hash chain as the authoritative state source. The paper also examines the Verifiable Credential lifecycle within MF+SO: issuance, storage, presentation, and revocation, with attention to the credential schema registry, proof format compatibility (Data Integrity Proofs, JSON Web Signatures), and the holder-binding mechanisms that prevent credential sharing. A comparative assessment evaluates MF+SO against three alternative decentra... Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores cryptography, key management in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.
Established threat modelling methodologies (STRIDE, PASTA, Trike, OCTAVE, LINDDUN, attack trees, and adversary-behaviour catalogues such as MITRE ATT&CK) were designed for software products and enterprises with a discernible security perimeter, a single owning organisation, and a clean separation between technical and operational risk. Modern organisations violate all three assumptions: they run on cloud and SaaS control planes they do not own, distribute privileged authority across founders, contractors, vendors, signers, committees, and automation, and expose value through human approval ceremonies and supply-chain edges rather than a network boundary. The dominant failures are authorised-but-malicious actors, collusion across nominally independent parties, control-plane and CI/CD compromise, and operational mishandling of high-value actions, which existing methods largely omit. We present TRACE, a methodology that treats threat actors, roles, assets, critical invariants, and trust/authority edges as first-class, evidence-linked objects spanning three layers: protocols, systems, and organisations. We compare nine widely used frameworks across ten dimensions, show where each falls short in distributed, cloud-first, zero-trust settings, and specify TRACE: its core model, three application pillars, sequential gated workflow, and an evidence-and-traceability discipline for human-AI co-working in which language models accelerate coverage while senior reviewers retain judgement over invariants, severity, and collusion. TRACE was developed through Web3 security practice but is stack-agnostic. We discuss its relationship to zero trust architecture and accountable Byzantine consensus, its limitations, and open questions around empirical validation.
This manuscript develops Dormant Continuity Theory (DCT), a protocol-relative mathematical framework for reasoning about systems that remain inactive at their protected core while retaining auditable continuity, recovery, diagnostic, and handoff capabilities. The theory formalizes dormant processes using finite transition systems, typed certificates, observable histories, evidence algebra, guarded authorization, replayable resolution, extraction adequacy, and fail-closed classification.DCT addresses practical challenges in long-lived distributed systems, including forked ledger histories, bounded model checking, data availability, zero-knowledge proof soundness boundaries, watcher incentives, MEV-resistant reward mechanisms, resource conservation, guardian corruption, maintenance transitions, and certificate-level HTLC handoff to extinction-style OSCT semantics. The framework distinguishes safety, bounded-griefing, diagnostic routing, and liveness assumptions, avoiding unconditional trustless claims while providing a rigorous finite core for verification and implementation-oriented extensions.
Chuanjia Yao, Zhihui Jiang, Xufeng Su, Xinchun Ma · 10 authors
The management of medical insurance funds is pivotal to the development of medical consortia. These funds serve as the operational lifeline of medical consortia and constitute critical public resources essential for public welfare. Medical expense settlement involves multiple stakeholders, including patients, tiered healthcare institutions, and insurance administrative agencies. However, disputes frequently arise between medical insurance authorities and hospitals regarding expense legitimacy due to information asymmetry and interpretative discrepancies. Such conflicts impede smoothness of payment mechanism, thereby undermining consortium operations and inter-institutional collaboration. To address these challenges, this study proposes a blockchain-based framework integrating medical expense investigation with insurance settlement. The system employs two core components: Anomaly detection via the Isolation Forest (IF) algorithm to identify potentially irregular expenses. Consensus-driven adjudication using Fleiss Kappa-based smart contracts facilitated by an anonymous panel of medical experts. This design enhances coordination between expense oversight and settlement processes, thus streamlining dispute resolution for medical expense anomalies and improving the scientific governance of insurance funds. Experimentally, the statistical validity demonstrated for unsupervised anomaly detection of IF and potential engineering applicability indicated in the analysis of healthcare cost. The healthcare consortium blockchain based on a Delegated Proof-of-Stake (DPoS) consensus mechanism and smart contract batch processing achieved a peak throughput of 229.64 transactions per second (TPS) and reduced transaction costs by up to 3,095 gwei. This demonstrates scalability for real-world medical insurance collaborative settlement systems.
Abstract Hybrid architectures in permissioned blockchains combining on-chain policies with Threshold Proxy Re-Encryption (TPRE) suffer from a structural audit gap: cryptographic enforcement is decoupled from the ledger's state. This allows Byzantine proxies to execute unaccountable data transformations (ghost requests). Furthermore, traditional TPRE access revocation incurs O(N) linear overhead, creating a scalability bottleneck.To address these flaws, we propose DT-Share, a state-bound access control middleware. By leveraging a novel State-Bound Evidence mechanism, DT-Share cryptographically anchors TPRE outputs to the global ledger epoch. This transforms access revocation from an O(N) key-management task into a constant-time O(1) ledger state transition.A full-stack implementation using Hyperledger Fabric and IPFS demonstrates that DT-Share guarantees strict accountability with negligible overhead. Under high-concurrency workloads, it exhibits an elasticity inversion phenomenon, adaptively scaling throughput during bursty requests to provide a highly scalable, Byzantine-resistant middleware for distributed consortia.
Bilateral attribute-based access control for data trading must hide policies, provide cryptographic fairness, and avoid trusted third parties. Existing solutions either leak policy information, incur super-linear costs, or rely on trusted dispute resolution. We present PriME-Deal, a non-interactive protocol that simultaneously achieves policy-hiding bilateral matching, efficient threshold access control, and auditable fair exchange on public blockchains. The seller embeds a secret token under the buyer policy into an oblivious key-value store with pseudorandom masking; the buyer reconstructs the token locally via tag-based probing, eliminating combinatorial enumeration, and proves correctness in zero-knowledge. Fair exchange is enforced through a collateralized on-chain reveal with a cryptographic audit that penalizes misbehaviour without trusted parties. We prove security in the Universal Composability framework under standard assumptions. Compared with the state-of-the-art threshold fuzzy IB-ME scheme, the seller's publishing time is reduced by two orders of magnitude (e.g., 8.76s vs. 690s for a policy of 500 attributes). For a typical configuration of (200,20,5), the buyer completes token reconstruction and proof generation in 8.9s, with the zero-knowledge proof taking under 0.6s and remaining constant across all parameter scales. The on-chain cost is approximately 28.6M gas, well within Ethereum's block limit. PriME-Deal thus delivers the first practical privacy-preserving data trading protocol that combines linear seller overhead, bilateral policy hiding, and auditable fairness.
Decentralized Autonomous Organizations (DAOs) enable blockchain-based collective governance, yet existing studies often evaluate DAO governance through isolated mechanisms, particularly voting systems. This narrow view does not sufficiently explain recurring problems such as governance capture, weak accountability, inadequate safeguards, and inefficient resource allocation. This paper proposes a Layered Governance Coverage Model that conceptualizes DAO governance as a system of seven interdependent institutional functions spanning participation, agenda formation, collective choice, safeguards, execution, incentives, and meta-governance. The model uses a four-level strength scale to assess not only whether governance functions are present, but also how strongly they are institutionalized. It is empirically applied to thirty-seven active DAOs through evidence-based coding of publicly available governance artifacts. The results show that governance breadth does not necessarily imply governance maturity: collective choice and execution mechanisms are more developed than accountability, safeguards, and meta-governance. Beyond DAO-native settings, the paper positions governance maturity as a trust and resilience regime for blockchain-based IoT and AI infrastructures, where governance affects security, reliability, data integrity, and risk oversight. The paper discusses AI-enabled governance analytics as a support mechanism for monitoring governance activity, detecting anomalies, and improving governance observability. The proposed framework contributes a structured approach for evaluating and designing resilient governance architectures in DAOs and blockchain-based IoT/AI systems.
Proof-of-Stake DAG-BFT consensus faces a trilemma between sybil resistance, reward fairness, and plutocracy. Existing protocols prioritize liveness over fair stake-based selection, driving longitudinal centralization. FairWave is a dual-channel DAG-BFT protocol that separates anchor selection from reward distribution. The selection channel is super-linear in stake, guaranteeing Sybil gain < 1 for K > 1; the reward channel is sub-linear via square-root stake normalization. DAG-derived uptime and latency metrics eliminate external oracles,and lagged reputation breaks circular dependency between selection outcomes and weights. Evaluated through approximately 550,000 Monte Carlo rounds against eight baselines, FairWave shows Gini 0.140 (vs. Pure-PoS 0.490, monotone HHI reduction from 0.039 to 0.020 over 50,000 epochs, and optimal Sybil split K * = 1. Safety follows unconditionally from the 2f + 1 commit rule; the liveness model predicts monotone degradation from 94.0% at b = 0.20 to 74.0% at b = 1/3, consistent with the architectural expectation of no discontinuous cliff.
This paper presents TrustBridge, the first universal decentralized trust protocol integrating multi-agent LLM consensus, zero-knowledge proof generation, blockchain attestation, and natural language accessibility for real-world credential verification across employment, education, healthcare, and supply chain domains. The multi-agent consensus engine runs three independent Claude Sonnet instances in parallel, achieving 92% adversarial detection on a controlled test set — a 30 percentage point improvement over single-agent architectures. Zero-knowledge commitment schemes allow claimants to prove credential properties without revealing private values. An ERC-721 NFT certificate provides immutable on-chain attestation. This is Paper 1 of a planned two-paper series. Paper 2 will report large-scale deployment results and full ZK-SNARK integration. Targeting: IEEE Blockchain 2027
LoisID proposes a portable trust and reputation infrastructure designed to enable individuals and organizations to accumulate, verify, and transport trust across digital ecosystems. The framework extends beyond identity verification and introduces a reusable trust layer for finance, education, employment, governance, and Web3 environments. By transforming trust into a portable and interoperable digital asset, LoisID seeks to address reputation fragmentation and establish a foundation for the next generation digital economy.
Trusted Execution Environments (TEEs) have emerged as a critical technology for safeguarding sensitive data and ensuring code integrity in modern computing systems. However, relying on a single TEE implementation makes systems vulnerable to a central point of attack. Building distributed-trust systems leveraging heterogeneous TEEs helps disperse trust but still faces threats from centralized management and adaptive mobile adversaries. To address these challenges, this paper introduces TeeDAO, a novel three-layer framework that automatically organizes multiple heterogeneous TEE instances and provides unified interfaces to support diverse applications, while ensuring long-term guarantees of availability, integrity, and confidentiality. TeeDAO couples BFT-ordered governance with heterogeneity-aware Distributed Proactive Secret Sharing (DPSS) and Secure Multi-Party Computation (MPC) so that attestation-driven committee changes are consistently reflected in secret recovery, resharing, and computation across a dynamic committee of heterogeneous TEEs. We implement a prototype of TeeDAO, integrating COBRA's DPSS scheme with the HotStuff BFT consensus protocol, and adapt it for Intel SGX, TDX, and Hygon CSV. Evaluations demonstrate that TeeDAO achieves up to 1.8x higher key-value store throughput in a large cluster with 61 nodes compared to state-of-the-art systems, efficient autonomous management, and minimal computation overhead (<18%) for multi-party computation tasks.
This technical note introduces the AIKernel Hash-Anchored Trust Layer (HATL), a hybrid trust architecture for Semantic Context Operating Systems and autonomous AI runtimes. HATL separates the trust boundary into an inner high-frequency symmetric ledger and an outer publicly auditable anchoring layer. The inner layer uses HMAC-SHA-512 and HKDF-based forward ratcheting to bind ReplayLogs, execution outcomes, and capability states with low runtime overhead. The outer layer aggregates local ledger commitments into Merkle roots and periodically anchors them using hash-based public signature mechanisms such as LMS, XMSS, and SLH-DSA. The report is distributed as a three-part technical package. Part I contains the full English manuscript and is the canonical version. Part II contains technical appendices, repository specifications, schemas, and reference implementation artifacts. Part III contains the Japanese companion translation. This version incorporates review-driven clarifications on secure erasure in C# / .NET environments, fail-closed handling of indeterminate governance decisions, and future integration of zero-knowledge proof techniques for public anchor verification. Documents are licensed under CC BY 4.0. Code, schemas, and contract specimens included in the appendices are provided under Apache-2.0.
Decentralized verifiable credential systems have seen limited deployment in practice. Existing constructions, built on zero-knowledge proofs, are complex, application-specific, and largely restricted to predicates over structured data. We present Privately Inferred Credentials ($Ï€$Creds): privacy-preserving, legacy-compatible, decentralized verifiable credentials generated by trusted LLM inference over authenticated data. LLMs' ability to semantically reason over unstructured data substantially expands the range of claims $Ï€$Creds can certify over existing credential systems. The use of LLMs also introduces new application-level threats, which we formalize through two problems: the Source-Constrained Adversarial Example (SCAE) problem, which captures robustness against adversaries that manipulate authenticated data to obtain misleading credentials, and the Authenticated Covert Predicate Poisoning (ACPP) problem, which captures privacy leakage through adversarial model selection. We characterize applications of $Ï€$Creds over user data, and a novel class of credentials over proprietary software that certifies properties of a service without revealing its source code. Our prototype supports issuing credentials over live financial, health, email, and code sources, and we empirically study the SCAE and ACPP threats on a product expertise credential over real financial data.
We present ENI6MA and Rosario Cypher as a proof-based identity and authorization architecture for emerging cybersecurity threats involving shadow AI, deepfakes, prompt injection, autonomous agents, credential theft, privacy exposure, and post-quantum risk. The paper responds to major 2026 cybersecurity forecasts by identifying a common root cause across many attack surfaces: conventional systems depend on reusable, stealable artifacts such as credentials, tokens, private keys, sessions, API keys, and stored personal data. ENI6MA replaces possession-based authentication with per-event proof of knowledge, policy-bound authorization, privacy-clean auditability, and contract enforcement behind cryptographically secure proof. Special attention is given to autonomous-agent security. The paper explains how ENI6MA constrains agents through per-action proof, verifier allowlists, policy identifiers, scoped pass credentials, and immutable validation records, reducing the risk of hijacked agents, excessive privilege, non-human identity sprawl, and zero-click prompt-injection exfiltration. The white paper also describes ENI6MA’s flexible deployment and capability model, including passwordless single sign-on, PII validation without disclosure, agent-to-agent authentication, proof-gated signing and custody, post-quantum sealing, sovereign/offline operation, and public verifier anchoring. This document is intended for cybersecurity leaders, AI governance teams, identity architects, privacy and compliance stakeholders, investors, technology partners, and researchers evaluating post-credential identity systems for human and autonomous-agent workflows.
Samukeliso Mabarani, Mohammad Saidur Rahman, Iqbal Gondal, H. M. N. Dilum Bandara
The tokenization of real-world assets (RWAs) through non-fungible tokens (NFTs) has introduced new opportunities for liquidity, enabling fractional ownership of traditionally illiquid assets. Yet, current NFT fractionalization models remain static, lacking adaptive governance and real-time responsiveness required for managing the dynamic nature of RWAs. This paper presents an Adaptive NFT Fractionalization Framework with Rights Segregation that integrates modular smart contracts, oracle data, and machine learning (ML) insights to enable dynamic rights management. The framework segregates and defines distinct rights, governed through cross-layer decision-making and adaptive rights management that updates allocations based on market data and predictive analytics. Experimental results demonstrate accurate, real-time adjustments of fractional rights, consistent governance execution, and efficient gas utilization across stress and concurrency tests. The findings validate the framework's scalability, responsiveness, and cost-effectiveness, establishing it as a viable approach for adaptive, data-driven management of fractionalized RWAs.