Isaac Amankona Obiri, Qi Xia, Jianbin Gao, Hu Xia · 5 authors
The growing adoption of electronic personal health records (ePHRs) demands cryptographic solutions that ensure secure and efficient data access. Threshold cryptography provides a framework for controlled multi-party access, yet existing schemes face practical limitations. Many require trusted key dealers, creating single points of failure and key escrow vulnerabilities, while others rely on pairing-based constructions that scale poorly. Furthermore, batch-oriented processing in previous schemes fails to support individual on-demand access patterns typical in healthcare applications. We propose a Distributed Identity-Based Threshold Decryption (DIBTD) scheme that addresses these limitations. First, our protocol removes all trusted setup assumptions through a fully distributed key generation mechanism based on verifiable secret sharing. Second, it achieves constant-time encryption and decryption operations, independent of committee size, by using efficient elliptic curve operations on secp256k1 rather than computationally heavy pairings, yielding up to 56× faster encryption than prior work. Third, DIBTD integrates the detection of malicious actors via zero-knowledge proofs, allowing the dynamic exclusion of compromised participants during system initialization. We provide formal security proofs showing the security of IND-CCA2 in the random oracle model under the discrete logarithm of the elliptic curve (ECDLP) and computational Diffie-Hellman (CDH) assumptions. The scheme remains secure against adaptive adversaries that control up to$t-1$participants. Experimental evaluation demonstrates practical efficiency: ciphertexts of only 86 bytes, constant 33-byte public keys, and sub-millisecond encryption latency. A pure Rust implementation on commodity hardware achieves 0.065ms per patient record while maintaining 128-bit security.
This study presents a comprehensive bibliometric analysis of blockchain identity management research published between 2010 and 2025, aiming to map its intellectual structure, thematic evolution, and global collaboration patterns. Using data retrieved from the Scopus database and analyzed with VOSviewer, the study applies network visualization, overlay visualization, density mapping, citation analysis, and co-authorship analysis to uncover dominant research streams and emerging frontiers. The results reveal that the field is conceptually centered on blockchain-based authentication and decentralized identity management systems, with increasing scholarly attention toward privacy-preserving mechanisms such as zero-knowledge proofs, anonymity, and data protection. Thematic evolution indicates a clear transition from foundational infrastructure-oriented studies to application-driven and regulatory-sensitive research domains, including e-government, IoT, healthcare, and digital governance. Collaboration analysis highlights the leading role of China and India, supported by strong transcontinental linkages with the United States and European countries, reflecting a globally interconnected yet regionally concentrated research landscape. By systematically mapping publication trends, thematic clusters, and collaboration networks, this study provides a structured knowledge base that supports future theoretical development, guides practical implementation, and informs policy formulation in blockchain-based digital identity ecosystems.
Wenjiang Shang, Hailing Li, Jun Wang, Chun Gui · 6 authors
The proliferation of mobile payments has brought about increasingly severe security challenges, including data breaches and identity forgery, which pose a significant threat to user assets and privacy. To meet the stringent security requirements of China’s multi-level protection scheme (MLPS) level 3 for financial systems, this study proposes an innovative privacy-enhancing protection scheme for mobile banking payments. This scheme is designed to provide comprehensive security throughout the entire lifecycle, from payment authentication to subsequent auditing. Specifically, our solution introduces two core mechanisms: the privacy-preserving authentication (PPA) protocol, which ensures the privacy of user identities and transaction data during the payment process by combining the private data access characteristics of oblivious RAM (ORAM) with zero-knowledge proof technology; and the distributed ledger audit mechanism (DLAM), which utilizes the decentralized and immutable features of blockchain, supplemented by ORAM, to guarantee the integrity of system logs and the privacy of the auditing process.
Deny Prasetyo, Siska Narulita, Ahmad Jurnaidi Wahidin, Rosalina Yani Widiastuti · 7 authors
This study introduces a trust centric machine learning framework designed to improve decision making reliability and security in decentralized digital service ecosystems. Traditional machine learning models often focus on accuracy and efficiency but fail to address the challenges of trust and security in decentralized environments. In contrast, the proposed framework integrates dynamic trust indicators and employs Federated Learning (FL) to ensure privacy while enhancing decision making performance. The framework also incorporates Zero Knowledge Proofp based Verifiable Machine Learning (ZKP-VML), which ensures transparency and security without compromising sensitive data. Through continuous real time trust assessments, the framework adapts to changing conditions, improving the accuracy and reliability of decisions in environments where participants may not fully trust each other. The application of this framework in autonomous vehicles and IoT networks demonstrated its ability to make robust, secure decisions, even in complex and uncertain scenarios. The framework’s ability to incorporate both trust and security into its decision making processes sets it apart from traditional models, which typically do not address the trustworthiness of data or participants. This research highlights the importance of integrating trust and security into machine learning models, particularly in decentralized systems, and offers a robust solution to trust management challenges. However, challenges such as scalability and computational efficiency remain, and future work should focus on enhancing these aspects, along with exploring the framework's applicability in other decentralized domains like finance or supply chain management. The integration of privacy preserving technologies and improvements in adversarial robustness are also potential areas for future research.
This comprehensive technical survey presents integration architectures for the Y.I.N. (Your Information Never leaves your control) Nine Pillars framework across 200+ commercial platforms spanning artificial intelligence (100+ LLM providers including OpenAI, Anthropic, Google DeepMind, Meta, Microsoft, Mistral AI, Baidu, Alibaba, Tencent), healthcare (50+ providers including Epic Systems, Tempus, PathAI), finance (40+ institutions including JPMorgan Chase, Goldman Sachs, BlackRock), autonomous vehicles (20+ companies including Waymo, Tesla, Cruise), telecommunications (25+ carriers including AT&T, China Mobile, Deutsche Telekom), and energy (20+ companies including Siemens Energy, NextEra) across 25+ countries. The Y.I.N. Nine Pillars architecture provides end-to-end privacy protection through: (1) Data Privacy (Differential Privacy), (2) Computation Privacy (Homomorphic Encryption), (3) Storage Privacy (Encryption at Rest), (4) Transmission Privacy (TLS 1.3), (5) Access Control (Zero-Knowledge Proofs), (6) Audit Trail (Merkle Trees), (7) Deletion Rights (Cryptographic Erasure), (8) Quantum Resistance (Lattice-based Cryptography), and (9) Token Licensing (Cryptographic Payment Enforcement). The Ninth Pillar token licensing system, covered by U.S. Patent Application 63/949,361 (filed December 28, 2025), provides cryptographic enforcement of usage rights by integrating token-derived blinding factors into homomorphic encryption operations, making computational correctness mathematically dependent on valid authorization. The system achieves 99.37% accuracy with valid tokens versus 50.7% with invalid tokens (t=147.3, p<10^-50), with security proven under CDH hardness (2^128 operations) and Ring-LWE assumptions. Integration schematics are provided for regulatory compliance with HIPAA (healthcare), SOX/DORA (finance), GDPR/EU AI Act (European Union), CCPA (California), PIPL (China), ISO 27001, NERC CIP (energy), and 15+ other frameworks. Extension directions are documented for community research including TEE hybrid architectures, MPC integration, VDF token lifetimes, key-homomorphic PRFs, flexible validation policies, hardware attestation, ABE capabilities, off-chain settlement, and DID/VC integration. Organizations seeking to implement these integration patterns may obtain licenses for individual pillars, sector packages, or the complete Nine Pillars system from the patent holder. Patent Notice: The Y.I.N. Nine Pillars architecture and Ninth Pillar token licensing system are covered by U.S. Patent Applications 63/949,361 (Ninth Pillar, filed December 28, 2025), 63/923,348 (QFED-MAZARI Quantum Extensions), 19/399,646 (Core Y.I.N. Architecture), 19/403,244 (Hardware Implementation), and 19/417,196 (SQL Database Integration), comprising 430+ claims across 15 patent applications.
Marwa Ali Hamdan AL-Jabri, Nafisa Abul Ghafoor Othman AL-Ansari
Access control is an important part of cybersecurity in distributed systems since conventional centralized mechanisms are not always sufficient. Due to blockchain, individuals have begun to employ decentralized access control models as they are capable of enhancing transparency, auditing and defending against fraud. At the reason of this report, we survey various blockchain-based access control systems, paying special attention to their architectures, confirmation mechanisms, identity models and policy enforcement mechanisms. We categorize the current literature into various groups based on their platforms (e.g. Ethereum, Hyperledger, Fabric), control mechanisms (e.g. RBAC, ABAC and capability-based) and whether they introduce additional privacy-tools such as zero-knowledge proofs and decentralized identifiers. The paper analyzes and describes the key gaps in current frameworks in terms of scalability, interoperability and computing expenses. Then, the shortcomings of the current research are pointed out so that they could guide future efforts in the field of blockchain-based access control systems.
Investigations of cybercrime today require forensic architectures that natively traverse multiple blockchains with ease while protecting and scaling evidence processing. Although blockchains support tamper- evident logs, their original single-chain architecture limits cross-platform interoperability and forensic scaling. Recent developments overcome these limitations such as zero-knowledge proofs supporting private but verifiable evidence verification, sharding architectures splitting state without compromising latency, and AI-based anomaly detectors identifying subtle tampering. But challenges remains like zero- knowledge proofs are computationally expensive, sharding poses intricate state-consistency problems and AI models need to be retrained constantly, incurring operational burden. Future research needs to make these pieces work for real- time, large-scale forensic applications by designing light-weight zero-knowledge constructs, self-tuning shard governance systems and compact AI with incremental-update threads. Integrating such abilities into single frameworks will offer privacy, scalability and security, supporting forensic processes for which courts will give credit in various, changing block-chain environments.
Sungmin Lee, Kichang Lee, Gyeongmin Han, JeongGil Ko
Many location-based services rely on a point-in-polygon test ( PiP ), checking whether a point or a trajectory lies inside a geographic zone. Since geometric operations are expensive in zero-knowledge proofs, privately performing the PiP test is challenging. In this paper, we answer the research questions of how different ways of encoding zones affect accuracy and proof cost by exploiting grid-based lookup tables under a fixed STARK execution model. Beyond a Boolean grid-based baseline that marks cells as in- or outside, we explore a distance-aware encoding approach that stores how far each cell is from a zone boundary and uses interpolation to reason within a cell. % Our experiments on real-world data demonstrate that the proposed distance-aware approach achieves higher accuracy on coarse grids with only a moderate verification overhead, making zone encoding the key lever for efficient zero-knowledge spatial checks. Our experiments on real-world data demonstrate that the proposed distance-aware approach achieves higher accuracy on coarse grids (max. % 60%p 60%p accuracy gain) with only a moderate verification overhead (approximately 1.4×), making zone encoding the key lever for efficient zero-knowledge spatial checks.
Abdullah Ayub Khan, Asif Ali Laghari, Hamad Almansour, Teerath Kumar · 7 authors
Wearable health technology has revolutionized remote monitoring and personalized healthcare by allowing real-time surveillance of patient health measurements and vital signs. However, their widespread acceptance is hampered by issues with security, privacy preservation, data protection, and interoperability. Blockchain Technology (BT), in particular Zero-Knowledge Proofs (ZKPs) and smart contracts, present a viable way to enhance the privacy, provenance, and integrity of wearable health data. This paper proposes a BT-enabled system that guarantees decentralized, unforged data management, transparency, immutability, and dynamic traceability for wearable health devices, particularly smartwatches with biosensors. To evaluate the effectiveness of the proposed work, the main performance-related metrics-latency, throughput, computational overhead, security robustness, and scalability-are looked at. The experiment's simulated findings show that BT integration is effective, with a 99.33% improvement in data integrity and protection. Automated access control protocols demonstrate data protection by utilizing smart contracts, and ZKPs guarantee verifiable data exchanges without jeopardizing patient privacy. These results demonstrate improved interoperability, decreased processing time, and increased security in comparison to comparable cutting-edge centralized platforms.
An effective healthcare data system must safeguard individual privacy, foster public trust, and enhance societal resilience. To achieve this, access to critical health information must be provided in an ethical, secure, and reliable manner. This paper proposes a blockchain-based healthcare management framework designed to improve security, privacy, and transparency in healthcare administration. The architecture incorporates smart contracts, multi-signature wallets, and zero-knowledge proofs (ZKPs) to securely facilitate key operations such as patient registration, policy updates, and medical device management on a decentralized platform. Multi-signature wallets require authorization from multiple stakeholders for sensitive transactions, while ZKPs enable identity or access verification without disclosing confidential information. A built-in performance monitoring module collects key metrics, including transaction latency, gas consumption, and block time, which are visualized using JavaScript. Overall, the proposed system offers a secure, transparent, and privacy-preserving approach to decentralized healthcare management.
We present the Mazari Bidirectional Architecture, a cryptographic framework providing end-to-end privacy guarantees for computation systems employing homomorphic encryption (HE), differential privacy (DP), and zero-knowledge proofs (ZK). The architecture comprises two complementary orderings: Y.I.N. Mazari Ordering (DP→ZK→HE) for secure data submission and Y.A.N.G. Mazari Ordering (VERIFY→DISAGGREGATE→DECRYPT→DENOISE) for secure result retrieval. Through exhaustive permutation analysis, we prove that among all possible orderings for each direction, exactly one ordering in each direction is secure—and these orderings are mathematical duals. The Y.I.N. ordering prevents information leakage during data submission, while the Y.A.N.G. ordering prevents timing side-channel attacks during result retrieval. This paper provides comprehensive coverage of all implementation variations, security proofs, and robustness analysis to establish technical foundations for privacy-preserving computation research.
Current commercial Large Language Model (LLM) architectures enforce a "server-side memory" paradigm, where user cognitive state is stored, managed, and monetized by the provider. This centralization creates two critical vulnerabilities: the economic inefficiency of "token inflation" (re-processing redundant context) and the epistemological risk of "rented cognition" (lack of user sovereignty over identity). This paper proposes a disruptive architectural shift: Remember Me AI, formally defined as the Client-Side Narrative Protocol (CSNP). By integrating Cross-Session Narrative Memory (CSNM) with a novel Semantic Compression Layer and Distributed Local Storage, we demonstrate a mechanism to reduce context token costs by approximately 40x while maintaining longitudinal coherence. We argue that this architecture commoditizes the inference layer, forcing a market transition from "Memory-as-a-Service" to "Compute-as-a-Commodity." This restores epistemological sovereignty to the user and neutralizes the lock-in mechanisms of hyperscale providers. The protocol includes Merkle-CRDT synchronization for multi-device consistency, Zero-Knowledge Safety Proofs for regulatory compliance, and a Polyglot Transpiler to ensure interoperability across proprietary model endpoints.
The fast digitalization of contemporary society has changed the data into a valuable resource, and it has been the key to the innovation in the financial sector, healthcare, politics, and industries, and it has also increased risks both in terms of misusing it, stealing it, and using it. Information security through maintaining confidentiality, integrity and availability of information has thus become a pre-requisite to trust in digital infrastructures. The present paper gives a detailed discussion of how cryptography, cybersecurity, and data privacy come into convergence and have a central role to play in protecting the digital ecosystems. Basic cryptographic primitives such as symmetric and asymmetric encryption, hash functions as well as digital signatures are discussed as the foundation of secure communication. With these, more complex privacy-sensitive technology like homomorphic encryption, zero-knowledge proofs, and differential privacy is discussed as technology that could offer the opportunity to perform safe computation and share data without jeopardizing the privacy of individuals.The paper also explores the disruptive potential of quantum computing, specifically how it can render the popular public-key systems insecure by figuring out ways to break them, e.g. the Shor algorithm, and assesses the new paradigm of post-quantum cryptography as a reaction to this existential risk. The examples are discussed within various fields such as secure communication schemes, data-at-rest security, cloud computing, and the Internet of things (IoT), e.g., in which cryptographic efficiency and versatility are most crucial. It is a synthesis of these factors that the paper highlights that cryptography is not only a technical protection but it is a cornerstone enabling resiliency, trust, and privacy-by-design in the digital era. This paper then ends with a discussion on the challenges that still need to be tackled, including scalability, usability and compliance with regulations, and how future research will be needed to define the future of secure and privacy-preserving technologies in the increasingly interconnected world.
Modern cloud-based AI training relies on extensive telemetry and logs to ensure accountability. While these audit trails enable retrospective inspection, they struggle to address the inherent non-determinism of deep learning. Stochastic operations, such as dropout, create an ambiguity surface where attackers can mask malicious manipulations as natural random variance, granting them plausible deniability. Consequently, existing logging mechanisms cannot verify whether stochastic values were generated and applied honestly without exposing sensitive training data. To close this integrity gap, we introduce Verifiable Dropout, a privacy-preserving mechanism based on zero-knowledge proofs. We treat stochasticity not as an excuse but as a verifiable claim. Our approach binds dropout masks to a deterministic, cryptographically verifiable seed and proves the correct execution of the dropout operation. This design enables users to audit the integrity of stochastic training steps post-hoc, ensuring that randomness was neither biased nor cherry-picked, while strictly preserving the confidentiality of the model and data.
Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs...
This paper proposes a hybrid IoT-blockchain architecture designed to ensure the security and value enhancement of flue gas desulfurization (FGD) gypsum throughout its entire lifecycle. At the edge, sensor data is encrypted using AES-256, with RSA-2048 handling key exchange, achieving a hybrid encryption overhead of 1.84 milliseconds per kilobyte. A permissioned Proof-of-Authority consensus mechanism delivers$\text{1, 7 0 0}$transactions per second with a confirmation time of just 0.59 seconds. An immutable ledger records purity, moisture, volume, and origin data; smart contracts automatically execute compliance checks and quality balance reconciliations. During a$\text{1 2}$-month field deployment at a 1.2-million-ton coal-fired power plant, the system reduced unauthorized access attempts by 94.7%, lowered transportation quality disputes by 80%, and improved downstream price stability by 18%. Scalable to 145,000 daily records, the system supports sub-second queries for$\text{8 5 {\%}}$of calls and achieves post-quantum security through zero-knowledge proof integration. This framework transforms industrial byproduct tracking into a verifiable, real-time asset valuation tool.
To address centralized trust risks, inadequate privacy protection, and quantum vulnerability of traditional crossdomain authentication systems, this paper proposes a quantumresistant self-sovereign identity (SSI) scheme based on lattice cryptography. Centered on Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), it constructs a decentralized ecosystem with two key innovations: a “single trapdoor - multi-attribute public key” structure (reducing storage and simplifying key management) and a lattice-based linkable ring signature (balancing anonymity and traceability). Implemented via TrapGen, SamplePre, and rejection sampling, the scheme's security relies on the Short Integer Solution (SIS) problem, with unconditional anonymity and unforgeability proven in the random oracle model. Efficiency comparisons confirm advantages in signature/verification time and storage overhead. This work provides a secure, privacy-preserving post-quantum solution for crossdomain collaboration in smart cities and supply chains. Future work will focus on lattice optimization and zero-knowledge proof integration.
Federated Learning (FL) enables distributed model training while preserving data privacy; however, it remains vulnerable to poisoning attacks and lacks computational integrity. Recent solutions integrating Zero-Knowledge Proofs (ZKPs) and blockchain have successfully established process-level verifiability but suffer from prohibitive computational overhead due to the requirement of generating cryptographic proofs for every local update. To address this efficiency bottleneck, this paper proposes Pro-ZkFL, a reputation-aware probabilistic verification framework. Unlike deterministic approaches that verify every transaction, Pro-ZkFL utilizes Verifiable Random Functions (VRF) on-chain to dynamically select a subset of clients for auditing based on their historical reputation scores. We design a dual-commitment scheme where clients submit lightweight cryptographic commitments for every round but generate heavy ZKPs only when challenged. Experimental results on FashionMNIST and CIFAR-10 demonstrate that Pro-ZkFL reduces computational overhead by approximately 82 % and gas costs by 73 % compared to full-verification baselines while maintaining a 99 % detection rate against persistent adversaries, offering a scalable trade-off between security and efficiency.
Elif Nur Kucur, Tolga Büyüktanır, Muharrem Ugurelli, Kazım Yıldız
Privacy-preserving machine learning (PPML) constitutes a core element of responsible AI by supporting model training and inference without exposing sensitive information. This survey presents a comprehensive examination of the major cryptographic PPML techniques and introduces a unified taxonomy covering technical models, verification criteria, and evaluation dimensions. The study consolidates findings from both survey and experimental works using structured comparison tables and emphasizes that recent research increasingly adopts hybrid and verifiable PPML designs. In addition, we map PPML applications across domains such as healthcare, finance, Internet of Things (IoT), and edge systems, indicating that cryptographic approaches are progressively transitioning from theoretical constructs to deployable solutions. Finally, the survey outlines emerging trends—including the growth of zero-knowledge proofs (ZKPs)-based verification and domain-specific hybrid architectures—and identifies practical considerations that shape PPML adoption in real systems.
Guilin Guan, Zhou Wg, Hongtao Xie, Yang Cao · 6 authors
The rapid advancement of big data and cloud computing technologies has elevated the importance of data transmission consistency verification in scenarios such as distributed storage, data backup, and content delivery networks. Traditional verification methods, including hash-based checks and digital signatures, inherently require access to raw data for computation and comparison. This dependency introduces risks of original data leakage and imposes substantial computational overhead in high-concurrency or large-scale data environments. Zero-knowledge proof (ZKP) technology offers a promising alternative by enabling a prover to demonstrate the validity of a statement to a verifier without disclosing any supplementary information. However, conventional ZKP schemes, particularly interactive ones, often suffer from complex communication rounds and significant computational burdens, rendering them unsuitable for data transmission scenarios demanding high real-time performance. This paper proposes a Lightweight Non-interactive Zero-Knowledge Proof (L-NIZK) protocol specifically designed for secure and efficient data transmission consistency verification. The protocol employs a data-blocking strategy combined with a Merkle tree structure and integrates an enhanced Pedersen commitment scheme with elliptic curve cryptography to achieve non-interactive and computationally efficient consistency proofs. A formal security analysis demonstrates that the proposed scheme satisfies completeness, soundness, and zero-knowledge properties under the random oracle model. Comprehensive performance evaluations indicate that the L-NIZK protocol surpasses existing mainstream solutions in proof generation time, verification time, and communication overhead, establishing its suitability for large-scale, high-concurrency data transmission environments.
This article examines the integration of blockchain, distributed file system, and virtualization technologies (including Virtual SAN, VSAN) to improve data storage privacy and efficiency. The limitations of traditional centralized storage models are presented, such as vulnerability to tampering, limited flexibility in access control, difficulty in auditing, and low resource efficiency. A concept for an integrated data storage system based on blockchain smart contracts is proposed. This system incorporates data encryption, implementation of an access control strategy based on blockchain attributes, the use of virtualization optimized through VSAN, distributed key management, and privacy-enhancing technologies (zero-knowledge proof, trusted computing environment). The proposed architecture provides flexible access control, efficient data processing, and a high level of security in a public or private blockchain environment.
Abstract In the realm of blockchains, synchronization challenges are two-folded. First, smart contracts from different blockchains cannot communicate with each other, making it hard to establish a trustworthy communication channel to share and maintain a universal state between each other. Second, transactions on different blockchains can hardly be ordered. Hence interference is expected. We need a novel way to handle interference. Traditional solutions involving third parties have safety and liveness issues and thus compromise between safety, permissionless, and liveness. ZK Multi-Blockchain Aggregatoris a multi-blockchain execution layer that leverages the power of zero-knowledge proof to minimize the trust base of multi-blockchain communication, which does not compromise safety, liveness, permissionless, and atomicity. In contrast to traditional blockchain bridges performing transactions on different blockchains separately and using a relay system to enforce the order of transactions and prevent interference, our method uses an entirely new approach, such that for each multi-blockchain transaction, it simulates the multi-blockchain transaction in its aggregator chain. Our aggregator uses zero-knowledge proofs of the simulation to convince involved blockchains to update their local state accordingly. On top of this layer, rich applications over multi-blockchains can run safely and efficiently.
Decision-OS V5 Revised (SiriusA2) addresses a practical AI safety problem: how human oversight can prevent irreversible decisions from being executed under pressure, confusion, coercion, or panic. It is designed for safety-critical, non-medical decision support settings where a user may be authenticated, yet the execution path may still be unsafe. The framework proposes a human-in-the-loop confirmation layer for irreversible risk. Instead of allowing a valid user action to move directly into execution, SiriusA2 routes protected actions through auditable confirmation states such as Request, Observe, Hold, Approve, Reject, Stop Candidate, Execute, and Revoke. The core mechanism is a trajectory-aware duress_score. This score is not an intent classifier, diagnosis, truthfulness score, or autonomous veto. It is an operational control-routing signal used to detect when a valid execution path deviates from an ordinary decision trajectory and approaches irreversible harm. SiriusA2 preserves human final consent through two-step confirmation, an explicit revoke path, optional family multisig, and a Zero-Knowledge approval layer (310/320) that verifies authorization without exposing personally identifiable information. The duress_score does not replace ZK approval or multisig; it routes actions into the confirmation path, while ZK qualification and multisig provide independent authorization conditions before irreversible execution. The revised manuscript integrates the SiriusA Adoption Gate into the main paper. The gate provides a Hold-first confirmation path for irreversible, externally pressured, unusually urgent, or high-stake actions: Request → Observe/Hold → Approve/Reject → Execute/Revoke. A score-based Stop Candidate does not automatically become Stop or Freeze. Stop or Freeze requires an independent non-score condition, such as verified revoke input, ZK-qualified m-of-k approval, policy-defined guardian confirmation, or an emergency protocol condition. If no such condition is available, SiriusA intentionally prefers continued Hold and evidence preservation over an AI-only execution veto. This release also clarifies cold-start behavior, causal bridge support, baseline maturity, corrected event terms, disclosure boundaries for calibration-sensitive parameters, and non-PII audit requirements. Safety is operationalized through auditable state transitions, non-PII KPIs, five-line gate outputs, evidence packaging (ZIP + SHA256), and explicit prohibitions on automatic transmission, payment, or reporting. A minimal proof-of-concept gate exists as a public runtime artifact, demonstrating PASS / DELAY / BLOCK routing, conservative severity merging, evidence union, pre-execution checking, and auditable JSON outputs. Deployment-level validation, calibration, and domain-specific robustness remain future work. Gateway / series index:https://github.com/shin4141/decision-os-paper Recommended read order:V5 Revised (SiriusA2) → V6 (PIC) → V8 (v2)Optional: V7 (AGI definition) Related repositories:- V5 Policy Pack / specification and adoption materials: https://github.com/shin4141/paper-public- Gate engine / MMAR-L0: https://github.com/shin4141/mmar-l0-core- SiriusA core runtime: https://github.com/shin4141/siriusA-core SSOT:GitHub repository “decision-os-paper”.This PDF corresponds to the revised SiriusA2 release candidate committed to the SSOT repository. Release note:This revised release integrates the trajectory-aware duress_score definition, SiriusA Adoption Gate, ZK qualification layer, family multisig, independent non-score Stop conditions, non-PII audit structure, V4-compatible escalation interface, and proof-of-concept gate positioning into the main paper. Transparency / Author’s Note:https://github.com/shin4141/decision-os-paper/blob/main/AUTHORS_NOTE.md
H. P. Yu, Yinglong Gao, Shen Su, Zhen Yang · 6 authors
Decentralized storage auditing approaches are designed to ensure data security in dishonest decentralized storage providers. However, the need for data updates introduces new challenges to the design of decentralized storage auditing approaches. Existing approaches can support dynamic auditing for updated files. Unfortunately, they can only deal with block-level updating, which is counter-intuitive and requires conversion from semantic changes to binary changes. Furthermore, existing dynamic auditing approaches require the recalculation of auxiliary auditing information (e.g., auditing authenticators) in data owners, which imposes unnecessary additional burdens on data owners, particularly those with constrained resources in decentralized storage environments. In this paper, we focus on image files and propose iAudit, an efficient pixel-level dynamic image auditing approach in decentralized storage. We first design a novel image authenticator with image pixels for efficient dynamic auditing, which combines convolution operations and polynomial commitment in authenticator construction. Additionally, we build an owner-free dynamic mechanism in dynamic decentralized storage auditing approach by utilizing zero-knowledge proof techniques. In this way, the dynamic operation overheads incurred by auditing can be completely eliminated from the data owners. A prototype of iAudit is implemented, and extensive experimental results demonstrate that iAudit outperforms state-of-the-art works, achieving over a 210× speedup for data owner in dynamic update phase.
Advanced Data Storage Technologies
Cloud Data Security Solutions
Advanced Steganography and Watermarking Techniques