Search Results (149)

IoT-connected healthcare sensor networks require authenticated and privacy-preserving data exchange among wearable sensors, mobile medical terminals, edge gateways, cloud servers, and medical institutions. Existing authentication schemes for healthcare IoT often bind signatures directly to user identities, exposing sensitive personal or institutional information and imposing heavy verification costs on resource-constrained sensing devices. To address this problem, we propose a fine-grained and flexible dual authentication scheme for healthcare sensor networks. In the proposed scheme, health data and diagnoses are signed with a fresh signing key and a fine-grained access control policy each time, so that the signer identity remains hidden while authorized entities can still modify permitted parts of signed data. No entity other than an authorized entity can trace a malicious signer or modify signed data without changing the data source. To support lightweight verification in sensor-edge-cloud deployments, we further present a verifiable outsourced authentication scheme that outsources time-consuming pairing operations to cloud servers; the online verification process then requires only six multiplication operations. As a fundamental technical component, we present a practical attribute-based sanitizable signature with shorter signature and key lengths and more efficient signing and signature-changing operations than the state-of-the-art policy-based sanitizable signature (P3S). Formal security analysis and experiments demonstrate the security and practicality of the proposed scheme for privacy-preserving healthcare sensing and medical data exchange. Full article

Image fusion integrates complementary multi-source visual information, yet plaintext fusion poses severe privacy risks. Conventional lattice-based homomorphic encryption enables ciphertext computation but incurs substantial computational overhead and exhibits poor compatibility with image fusion tasks. This work investigates lightweight privacy-preserving image fusion built upon pixel scrambling. Any pixel-scrambling technique that only rearranges pixel coordinates without modifying pixel values inherently satisfies the homomorphic properties required for pixel-level spatial fusion. In this paper, we adopt full-size random permutation matrix scrambling as a representative pixel-disordering method for systematic theoretical and experimental verification. The scheme generates a secret key matching the resolution of test images; it merely reorders pixel positions while preserving all original intensity values, allowing direct cipher-domain fusion that yields distortion-free outputs for averaging, weighted averaging, maximum-value and minimum-value fusion rules. Free from intricate lattice calculations and ciphertext expansion, the proposed lightweight framework achieves an optimal trade-off among security, computational efficiency and fusion quality for cloud computing scenarios. Full article

Mesenchymal stem cells (MSCs) are a critical biological resource for regenerative medicine, immunomodulation, and personalized cell therapy. Three structural problems persist: (1) the absence of standardized, quantitative quality indicators; (2) insufficient tamper-proof traceability throughout the manufacturing and banking lifecycle; and (3) the lack of a personalized matching system linking MSC batch characteristics to patient-specific clinical requirements. This paper proposes the MSC Digital Assetization Framework (MDAF), an applied engineering framework that addresses all three problems at the architectural and prototype level. Here, digital assetization—the transformation of a biological product into a structured, traceable, and transferable digital quality record within a multi-institutional trust infrastructure—denotes verifiable, traceable, quality-certified digital recordization of MSC batches, not tokenization or financial trading. The quality engine integrates morphological, FLIM-derived metabolic–proliferative, donor blood panel, flow cytometry, and manufacturing metadata inputs through a bidirectional Cross-Attention fusion module, yielding a continuous MSC quality score (MQS, 0–100) and an S/A/B/C/D five-tier grade. Privacy-preserving verification is implemented via two independent Groth16 zero-knowledge proof circuits: a Release Eligibility Proof (REP, MQS ≥ 70) and a Premium Quality Proof (PQP, MQS ≥ 85). A Hyperledger Besu QBFT permissioned blockchain with smart contracts provides immutable lifecycle traceability and DID-based access control. In a synthetic data pilot (n = 2000), the system demonstrated engineering feasibility across all five subsystems. These results are engineering pipeline feasibility benchmarks on synthetic data; biological and clinical validation using real MSC data is mandatory follow-on research. Full article

The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends. Full article

IoT sensor deployments increasingly export measurement streams to edge gateways and content-addressed storage such as IPFS, but access control decisions must be enforced without disclosing sensor owner policies, requester attributes, or stale data versions. Existing blockchain, CP-ABE, and zero-knowledge approaches reduce parts of this leakage, yet they can still expose public policy structure, accept stale Merkle proofs after sensor stream updates, overload provers when policies grow, or leave IPFS gateways vulnerable to bandwidth abuse. This paper proposes zk-Guard-R, a policy-hidden and replay-safe zk-SNARK access control framework for privacy-preserving IoT sensor data sharing. zk-Guard-R replaces public sparse policy matrices with MiMC-Merkle policy commitments verified inside the proof, separates long-lived logical sensor policy roots from frequently updated physical IPFS data roots, binds every proof to an on-chain nonce, and decouples attribute possession from policy interpretation through a bounded stack-based policy interpreter. Numeric sensor-access predicates are represented through committed values and range check gadgets, while an off-chain verification gateway couples accepted proofs with payment channel vouchers before releasing encrypted IPFS chunks. The design contribution is separated from the measured prototype: the full protocol specifies a bounded policy interpreter, whereas the present gnark prototype evaluates the core committed policy, committed attribute, range check, data root, nonce, Solidity verifier, and gateway-metering mechanisms. We implement a gnark BN254/Groth16 research prototype and benchmark it against a matrix-public zk-Guard prototype, a blockchain ABAC baseline, an IoT token/HMAC baseline, and a CP-ABE-style cryptographic-work proxy. For 128 attributes, the zk-Guard-R prototype with MiMC-Merkle commitments uses 425,574 R1CS constraints, generates proofs in 3.12 s, verifies in 0.73 ms, and uses 641 MB peak Go heap allocation. A three-run repeat of the 128-attribute configuration gives a proof-generation mean of 2.80 s with a 0.54 s standard deviation on the same local host, illustrating the runtime variability of prover measurements. We also deploy the generated Solidity verifier on a local Anvil EVM and measure 241,942 gas for a successful verification transaction, and we evaluate a local Kubo/IPFS gateway under valid, replayed, and voucher-limited flood requests. The results show that zk-Guard-R shifts substantial but measurable work to the prover while improving policy confidentiality, freshness, and gateway metering for IPFS-backed IoT sensor data sharing. Full article

Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains, where poisoning attacks can corrupt training data, manipulate model updates, or implant covert backdoors. This survey examines poisoning attacks in federated learning (FL), using centralized learning as a baseline to explain how distributed data, client heterogeneity, privacy-preserving aggregation, and untrusted coordination expand the threat surface. It positions prior surveys and synthesizes representative primary studies through an accountability-oriented lens focused on attribution, audit evidence, traceability, and forensic readiness. The review compares major attack classes, including data poisoning, model poisoning, backdoor insertion, server-side manipulation, Sybil behavior, collusion, and multi-round poisoning. It also evaluates countermeasures such as Byzantine-robust aggregation, anomaly detection, validation-based filtering, malicious-secure aggregation, authenticated update handling, provenance mechanisms, ledger-based evidence, and verifiable aggregation protocols. The analysis shows that robustness alone is insufficient for trustworthy FL unless defenses also preserve evidence that supports independent verification, post-incident reconstruction, and governance review. Persistent gaps remain in causal forensic attribution, privacy-preserving evidence governance, malicious-server threat modeling, scalable verifiability tooling, recovery after poisoning, and deployment-ready benchmarks. The survey concludes that accountable FL should be designed as an evidence-producing system, not merely as a privacy-preserving or attack-resistant training architecture, especially for regulated, cross-silo, and high-risk real-world deployments. Full article

Background: Maritime container shipping carries over 80% of global trade, yet compliance verification creates a confidentiality–verifiability conflict: carriers treat telemetry as commercially sensitive, while regulators, insurers, and port authorities require verifiable proof that cargo remained within specification. The EU Ecodesign for Sustainable Products Regulation (ESPR) mandates Digital Product Passports (DPPs), but no standardised DPP architecture exists for the multi-stakeholder maritime domain. Methods: We present Ocean DPP, a blockchain-anchored platform combining GS1 EPCIS 2.0, oneM2M, IOTA, and Groth16 zero-knowledge proofs (ZKPs), letting stakeholders verify compliance predicates without revealing raw sensor values; Merkle-tree batching reduces anchoring costs. We evaluate it in 16 experiments on a single-host testbed using synthetic workloads and a local IOTA network. Results: The platform achieved 95th-percentile latency of 48 ms without ZKP and 500 ms with proof generation, throughput of 7 events/s per host, 304 ms mean proof generation and 9.8 ms verification, 100% EPCIS 2.0 compliance, and zero permanent message loss across four failure-injection scenarios; horizontal scaling reduced the median latency by 37%. Conclusions: To the best of our knowledge, Ocean DPP is the first implemented, quantitatively evaluated platform integrating EPCIS 2.0, oneM2M, IOTA, and Groth16 ZKPs for privacy-preserving maritime DPPs; broader multi-host and public-network validation remains for future work. Full article

This paper presents AERO-GUARD, a formally verified drone authentication and road surveillance system that integrates Kyber post-quantum key encapsulation, physical unclonable functions (PUFs), decentralized IPFS-based identity storage, and blockchain-anchored audit logging. AERO-GUARD operates across three phases, key provisioning, enrollment, and authentication, enforcing mutual authentication, replay resistance, and privacy-preserving comparison through an off-chain evaluator (OCE) that performs homomorphic subtraction on encrypted PUF responses without accessing plaintext secrets. The protocol is modeled and verified using ProVerif 2.05 under the Dolev–Yao adversary model. To evaluate the system beyond theoretical verification, a simulation environment was developed to replicate realistic road conditions, incorporating a simulated road network and a virtual drone traversing monitored routes. An AI model is deployed to perform real-time detection of suspicious and anomalous activities along the road. All detection events are surfaced through a centralized monitoring dashboard that provides authorized personnel with live alerts, a drone camera livestream with detection annotations, and contextual drone telemetry, enabling timely and informed incident response. Formal verification results demonstrate that AERO-GUARD satisfies the targeted security properties, including mutual authentication, secrecy preservation, and replay resistance, confirming the protocol’s resilience against common authentication attacks. Full article

In response to the current research landscape, which lacks unified quantitative standards and a reproducible verification framework for assessing the security of quantum neural networks (QNNs), this paper proposes a quantitative evaluation system and verification scheme for QNN security based on the violation characteristics of CHSH-type Bell inequalities. This method treats quantum entanglement as the core element of intrinsic security and establishes a controlled-variable controlled experiment involving purely classical models, non-entangled QNNs, weakly entangled QNNs, and strongly entangled QNNs. Numerical simulations were conducted using the Iris dataset; the presence of quantum entanglement was determined using the CHSH statistic, and a quantitative metric system centered on the normalized CHSH observation metric Q was constructed. Based on the theoretical limits of Bell’s inequalities, a normalization derivation was performed to establish the theoretical constraint interval of $0\le \mathrm{Q}\le 1$; the threshold values of 0.3 and 0.7 obtained from the simulations are applicable only to the experimental scenarios described in this paper and serve solely as a reference for grouping data within the experiment; they do not possess universal validity for determination. Simulation results show that the CHSH values for the weakly and strongly entangled QNN experimental groups can reach 2.8284, significantly exceeding the theoretical limits of classical locality. These values correspond to an observation metric of $Q=0.9838$ and a privacy protection strength of $P=0.8854$, with the security level rated as high. The CHSH values of the non-entangled QNN and the purely classical model do not exceed the classical threshold of 2; they exhibit no quantum nonlocality or quantum advantage, and their security level is rated as low. This paper advances the security evaluation of QNNs from qualitative, empirical judgments to verifiable quantitative classification, providing a theoretical basis and evaluation framework for the practical application of quantum neural networks in highly security-sensitive scenarios such as privacy-preserving computing. Full article

Vehicular ad hoc networks (VANETs), as a key communication component of the Internet of Vehicles (IoV), enable vehicles and roadside infrastructure to exchange information efficiently, thereby supporting road safety and traffic management. However, because these communications take place over open wireless channels, VANETs are exposed to message forgery, replay, identity disclosure, and unauthorised access by revoked vehicles. To address these issues, this paper proposes EPAF, an efficient privacy-preserving batch authentication scheme with revocation support for fog-enabled VANETs. EPAF uses roadside fog nodes to distribute update keys and report information related to misbehaving vehicles, thereby reducing reliance on remote centralised processing. Rather than assuming ideal tamper-proof devices that store system-wide secrets, EPAF requires protected storage only for vehicle-local certificates, limiting the impact of compromising an individual vehicle device. The scheme employs batch verification to authenticate multiple messages from different vehicles in a single procedure, reducing verification overhead in message-intensive traffic conditions. It further introduces an update-key mechanism through which legitimate vehicles obtain current authentication keys, whereas revoked vehicles are prevented from generating valid authentication messages in subsequent revocation periods. Under the honest-authority model, the security analysis establishes the EUF-CMA security of an authentication packet in the random-oracle model and separately addresses conditional identity privacy, traceability, and unlinkability across different pseudonym periods. Performance evaluation examines the trade-off among authentication efficiency, communication overhead, and revocation performance, showing that EPAF is a practical solution for fog-enabled vehicular communication. Full article

Automated recognition of identity documents is a critical component in digital identity verification systems. The development of robust recognition models is often constrained by the limited availability of large, diverse, high-quality, and publicly accessible ID card datasets. Collecting and annotating real-world ID card images is time-consuming, and often restricted due to privacy, legal, and security concerns. The paper proposes a novel approach for generating a large-scale synthetic dataset for ID card recognition by merging real ID card images with a texture dataset through a structured data fusion pipeline that introduces realistic visual variations as illumination effects, geometric distortions, and noise patterns while preserving the semantic integrity of the original ID card content. Full article

Heterogeneous cross-domain networks are plagued by fragmented trust, data silos, and privacy risks, while conventional single-chain architectures fail to reconcile cross-chain interoperability, regulatory compliance, and commercial privacy. To address these limitations, we present an Entity–Data–Asset triple-verification architecture that integrates three key components: Decentralized Identifiers (DIDs) for identity, Verifiable Credentials (VCs) for credentials, and Oracles for cross-chain coordination. Specifically, this architecture enables trustworthy collaboration through three core mechanisms: (1) a cross-chain identity binding mechanism based on DIDs that replaces traditional address binding to construct an “identity-as-access” trust model; (2) a collaborative verification paradigm leveraging VCs and Verifiable Presentations (VPs) to cryptographically link off-chain verification with on-chain execution; and (3) an event-driven Oracle coordination matrix designed for complex business semantics, supporting automated and privacy-preserving state synchronization across asymmetric domains. Experimental evaluation of a prototype integrating Hyperledger Indy and Besu demonstrates a peak Verifiable Presentation batch verification throughput of 0.96 batches/s at $C=20$, though throughput degrades noticeably under high concurrency due to middleware contention, and an average cross-chain transfer latency of 13.31 s under single-process conditions (mean over 10 iterations). Furthermore, by leveraging this asymmetric design, our architectural optimization strategy—anchoring only cryptographic hashes rather than full credential payloads—reduces the regulatory chain’s storage and gas overhead by approximately 85% compared to traditional full-payload schemes. These results validate the architecture’s feasibility, security, and cost-efficiency for facilitating trustworthy collaboration in complex, heterogeneous ecosystems. Full article

The outsourcing of machine learning tasks, such as Support Vector Machine (SVM) training, to cloud platforms poses significant security challenges, primarily concerning the confidentiality of sensitive training data and the integrity of computation results returned by potentially malicious servers. To address these challenges, this paper proposes a lightweight, privacy-preserving, and verifiable SVM training scheme designed for resource-constrained clients. Our scheme leverages a replicated secret sharing protocol to securely distribute training data and model parameters across multiple non-colluding servers, executing the entire collaborative training process in the share domain without leaking plaintext information. Furthermore, to guarantee computational correctness, we introduce a novel interval-based index point storage strategy combined with a bilinear mapping-based parameter label consistency check. This verifiable mechanism enables clients to perform sampled, lightweight audits of the cloud’s intermediate training states and final outputs. Experimental evaluations on multiple typical datasets demonstrate that the proposed scheme maintains stable classification performance while achieving an order-of-magnitude decrease in training runtime compared with existing ciphertext-based methods, offering a highly configurable trade-off among verification coverage, computational overhead, and storage cost. Full article

Information technology service management (ITSM) systems generate large volumes of unstructured incident descriptions. They frequently include multilingual content, code-switching, informal language, and domain-specific terminology. These characteristics make automated text processing substantially more complicated and limit the applicability of conventional machine translation solutions, particularly in environments subject to strict data privacy and air-gap constraints. This paper presents a system-level reproducibility study of a deterministic two-stage cascaded neural machine translation (NMT) pipeline for normalizing multilingual IT incident text in resource-constrained, air-gapped environments. The study evaluates a sequential RU→EN and LT→EN translation strategy specifically selected to bypass unreliable language identification, enabling stable processing of code-switched incident descriptions. A system-level processing pipeline, which includes text normalization, segmentation, deduplication, adaptive batching, and language-aware data flow optimization, is analyzed to assess its impact on reducing redundant inference operations. The methodology is evaluated on a real-world ITSM dataset comprising 84,285 incident records. An incremental experimental design is used to isolate the specific contributions of computational and data-flow optimizations. Translation quality is assessed using BLEU and COMET metrics against expert reference translations produced via a primary translation and subsequent cross-verification by a second domain expert to ensure linguistic and technical consistency. The results indicate that a cascaded NMT architecture combined with systematic data-flow optimization provides a reproducible and privacy-preserving framework for multilingual IT incident text normalization, effectively supporting downstream analytical tasks in constrained operational ITSM environments. Full article

The digital sharing of healthcare data necessitates a careful balance between the need for verifiability and the protection of patient privacy. In many real-world scenarios, particularly in employer and third-party verification processes, excessive clinical information is disclosed beyond what is strictly required. This practice introduces significant privacy risks and conflicts with data minimization principles. To address this problem, this study proposes a blockchain-based, privacy-preserving system architecture that enables health report verification without revealing diagnosis information. The proposed system is built upon a dual-layer architecture that structurally separates clinical data from verification processes. In the clinical data layer, health reports are encrypted on the client side and stored in off-chain environments, while only reference data and access control information are recorded on the blockchain. The system further integrates revocation mechanisms, role-based access control, and auditability through a modular smart contract design. In conclusion, this study introduces a modular, privacy-oriented, and practically applicable solution for secure healthcare data verification. By eliminating the need for clinical data disclosure during verification, the proposed architecture offers a novel design perspective and contributes both conceptually and technically to the development of blockchain-based healthcare information systems. Full article