In the context of sustainable waste valorization, we investigated microbiological safety hazards associated with spore-forming bacteria, focusing in particular on Bacillus cereus contamination and diversity during Hermetia illucens (Black Soldier Fly, BSF) bioconversion at different developmental stages. Larvae were reared on authorized (carrots, apricots, wheat bran) and unauthorized substrates (expired unpackaged retail food products and cafeteria waste). After 7 days of spontaneous fermentation of these substrates, total aerobic mesophilic bacterial counts were comparable across substrates; however, aerobic spore counts were significantly higher in unauthorized than authorized substrates. Notably, B. cereus was undetectable in authorized substrates but reached 5.2-6.0 log CFU/g in unauthorized waste. Despite substrate differences, BSF larvae and frass showed similar levels of total mesophilic and spore counts, with B. cereus consistently detected, indicating that vertical transmission may take place. Phylogenetic analysis revealed the persistence of potentially enterotoxigenic B. cereus group III strains harboring nheI and cytK genes throughout the bioconversion process. Additionally, group IV strains initially present in the substrate became dominant in larvae and frass by day 14. These findings demonstrate that BSF larvae are unable to significantly reduce B. cereus during entomoconversion. Frass and larvae from both authorized and unauthorized substrates showed similar contamination levels. Both types of substrates were suitable for larval rearing and did not lead to significant differences in B. cereus levels, however, the persistence of high loads in larvae and frass, regardless of the substrate, may represent a potential health hazard in the absence of further treatment.
Electric Vehicles (EVs) that use Internet of Things (IoT) networks often involve the exchange of sensitive data between vehicles, charging stations, and other infrastructure, making data security and user privacy critical concerns. Existing methods for securing data in EV IoT networks rely on centralized systems, which create a single point of failure and are vulnerable to cyberattacks, data breaches, and unauthorized access. Furthermore, these systems struggle to address privacy concerns effectively, especially regarding user location and personal information. The proposed solution introduces a Blockchain Technology-based privacy preservation framework for EV networks (BCT-PP-EV). This framework leverages blockchain's decentralized nature to provide secure, transparent, and tamper-proof data exchanges. It ensures user privacy using cryptographic techniques such as zero-knowledge proofs (ZKP) and data anonymization, allowing privacy-preserving transactions without compromising data accuracy. Blockchain's immutability guarantees the integrity of the shared data, while smart contracts automate secure and efficient interactions within the network. The proposed method enhances secure data sharing while preserving privacy across EV IoT networks. By decentralizing data storage and enabling transparent auditing, BCT-PP-EV fosters trust among stakeholders and reduces the risks of unauthorized access or data manipulation. Preliminary findings suggest that implementing BCT-PP-EV significantly improves the security and privacy of data exchanges in EV networks, providing a scalable and resilient solution for the evolving smart transportation ecosystem. Experimental results demonstrate that BCT-PP-EV achieves 94.91% secure data sharing efficiency, reduces data breaches by 92.84%, and ensures data accuracy of 91.44%. Additionally, the framework exhibits high scalability of 96.57% with increasing network nodes, while maintaining controlled latency and throughput. Although unauthorized access resistance is measured at 24.71%, indicating scope for further improvement, the overall results confirm that BCT-PP-EV provides a robust, scalable, and privacy-preserving solution for next-generation smart transportation systems.
In recent years, there has been a significant shift from host-centric to data-centric networking across various digital sectors and critical environments. Named data networking (NDN) is the most prominent paradigm of data-centric networking implemented in real-world applications. The healthcare industry is considered one of the most prominent sectors to have adapted to this transformation. While NDN-based healthcare systems have demonstrated significant advances in building data-centric security frameworks, the expansion of interconnected medical systems has introduced novel security breaches, such as unauthorized access to medical data. Therefore, continuous improvements to access control mechanisms in NDN-based healthcare systems are extremely important. Most of the existing NDN-based access control mechanisms lack comprehensive integration of security deployment, in-network caching, and distributed content retrieval. To address this issue, this study proposes a decentralized authorization framework for NDN-based healthcare systems, providing fine-grained access control to medical data. The proposed framework integrates a smart contract-based access control (SCBAC) mechanism to provide an attribute-based and policy-driven enforced data access approach and proxy re-encryption (PRE) to enable secure delegation of decryption rights without exposing original data. The evaluation of the proposed framework encompassed three main aspects: performance, security, and scalability, under three different load conditions for scalability testing. The results of the simulation experiments show that the proposed framework achieves a response time of around 132 ms, maintains more than 94% accuracy in access success and denial rates, and accurately delegates to authorized entities with 97.4%-96.5% accuracy, while incurring re-encryption overhead of up to 8 ms. These outcomes improve the response to multiple cyber threats, including unauthorized access and breaches of medical data in dynamic NDN-based healthcare environments.
Limited domestic cotton production in Japan has led to large amounts of cottonseed being imported for food and feed. Consequently, genetically modified (GM) cotton seeds have been introduced into the country, and a qualitative method is required for the detection of unauthorized GM cotton. The aim of this study is to develop a testing method that is simpler, more rapid, and more cost-effective than conventional polymerase chain reaction (PCR) technique. A novel method was developed for the screening detection of unauthorized GM cotton seeds using loop-mediated isothermal amplification (LAMP) to target the 35S promoter of the cauliflower mosaic virus (P35S), neomycin phosphotransferase Ⅱ (NPTII), and the endogenous cotton gene stearoyl acyl carrier protein desaturase (sad1). The method limit of detection (LOD) was determined to be ≤ 0.1%, which is equivalent to the corresponding LOD achieved using the conventional PCR method. A simpler detection method based on single-stranded tag hybridization on a chromatography-printed-array strip system (denoted DNA chromatography) was also developed. This method allowed visual detection of the amplification of multiple DNA samples at the LOD level. Compared with the conventional PCR approach, the developed detection method is cost-effective, rapid, and simple. The developed method facilitates the analysis of GM crops and will be expected to help prevent the unintended spread of GM crops into the environment.
Fire accidents in public assembly occupancies often arise from complex interactions among institutional failures, behavioral violations, and physical hazards. Traditional linear analyses cannot capture these dynamic and systemic causation mechanisms. Drawing on 185 official investigation reports of major fire incidents in China from 1998 to 2020, this study builds a Fire Accident Causation Complex Network Model (FACCNM). The model integrates the Apriori algorithm, complex network analysis, and the 24 Model to identify key contributing factors and high-risk causal chains in fire accidents. The findings show that "unauthorized design change" and "renovation with flammable materials" occur frequently and occupy structurally central positions in fire risk propagation. The highest-risk causal chain, "Absence of fire audit/acceptance → unauthorized design change", illustrates a typical pattern in which institutional oversight triggers behavioral and material vulnerabilities. The results offer actionable guidance for fire risk governance, such as prioritizing intervention points, conducting targeted safety audits, and designing proactive regulatory strategies. This study also helps shift fire safety management from reactive response to preventive control, especially in high-density public environments.
The transfer of patient information in a networked health care system raises significant security issues, including unauthorized access, tampering, and unauthorized use. Most of the techniques have shown limited performance in terms of robustness, imperceptibility, and high embedding capacity simultaneously. Thus, to overcome these problems, this research has introduced a robust watermarking scheme for smart health care. The method proposed in the paper follows a hybrid approach, combining concepts of visible and invisible watermarking, multimodal image fusion, and encryption to enhance the system's reliability and security. First, the hospital logo is inserted as a visible watermark in the carrier image. Then, the CT and MRI images are fused together through the hybrid NSST-DTCWT approach to create the medical watermark. To improve efficiency, PCA is applied to the visibly watermarked carrier image. The fused watermark is then inserted into the system using the NSST and SVD approaches, focusing on low-energy areas to achieve high imperceptibility and robustness. The process is then followed by the encryption process for the watermarked image. The extensive experimental evaluation and comparison confirm that the proposed framework possesses excellent adaptability, robustness (Average NC[Formula: see text]), and embedding efficiency, with high visual quality (Average PSNR and SSIM of 44.7122 dB and 0.9955, respectively). The results' findings indicate that the proposed watermarking technique is effective for tackling the security, transparency, and robustness concerns in medical image communication, thereby making it a feasible and clinically applicable tool in smart healthcare applications.
Printing custom DNA sequences is essential to scientific and biomedical research, but the technology can be used to manufacture plagues as well as cures. Just as ink printers recognize and reject attempts to counterfeit money, DNA synthesizers and assemblers should deny unauthorized requests to make viral DNA that could be misused. There are three complications. First, we do not need to quickly update printers to deal with newly discovered currencies, whereas we regularly learn of new potential pandemic viruses and other biological threats. Second, convincing counterfeit bills cannot be printed in small pieces and taped together, while preventing the distributed synthesis and subsequent re-assembly of controlled sequences will require tracking which DNA fragments have been ordered across all providers and benchtop devices while protecting legitimate customer privacy. Finally, counterfeiting can at worst undermine faith in currency, whereas unauthorized DNA synthesis could be used to deliberately cause pandemics. Here we describe SecureDNA, a free, privacy-preserving and fully automated system capable of verifiably screening all DNA synthesis orders of 30+ nucleotides against an up-to-date database of controlled sequences, and its operational performance and specificity when applied to 67 million nucleotides of DNA synthesized by providers in the USA, Europe and China.
Model fingerprinting, embedding user-specific identifiers (fingerprints) into generated outputs, has recently emerged as a popular solution to protect the intellectual property rights (IPR) of generative text-to-image (T2I) models and prevent unauthorized redistribution. In this work, we reveal a previously unexplored systematic vulnerability in existing generative model fingerprinting methods: they lack robustness against collusion attacks, where multiple attackers combine their models to remove or obscure the fingerprints. To address this issue, we take the first step towards a robust fingerprinting method for T2I models with anti-collusion capabilities. The proposed method encodes strings of bits, namely fingerprints, into the coefficients of a personalized normalization module (PNM) incorporated into T2I models, so that fingerprints can be reliably recovered from any generated image. To defend against collusion attacks and prevent unauthorized model redistribution, we introduce an anti-collusion mechanism based on lossless function-invariant parameter transformations. This mechanism significantly degrades the image generation quality of colluded models, making them effectively unusable. Moreover, our method allows developers to efficiently create multiple copies of fingerprinted T2I models by reparameterizing the PNM without the need for retraining. We also introduce a worst-case optimization strategy to improve robustness against model-level attacks. Our experiments demonstrate that the proposed method achieves high fidelity and robustness across multiple T2I image generation and editing tasks, with fingerprint extraction accuracy exceeding 99.5%. Compared with existing methods, our method demonstrates, for the first time, a notable proactive robustness to collusion attack by significantly increasing the FID of colluded models.
Smart grid data hubs face multifaceted security challenges during data sharing due to the intertwined nature of personal privacy and commercial secrets within grid data. These challenges include external attacks, unauthorized internal access, and insufficient transmission encryption. Existing single-dimensional security mechanisms struggle to address these issues. Therefore, this study investigates a secure data sharing method for smart grid data hubs that employs combined keys within a trusted controlled environment. For data sharing within the smart grid data middle platform, a blockchain-based secure sharing model is constructed involving five types of entities, including certificate authorities and data owners. This model standardizes the entire process from initialization and data preprocessing to publication and retrieval. During the sharing management process, a trustworthiness calculation model is designed. Trust certificates are constructed by combining user misconduct records with service interaction frequency to compute user violation probability and trustworthiness, thereby dynamically allocating user access permissions. A combined key generation method is employed, where dynamic keys are generated based on a key seed matrix combined with timestamps and random numbers. These keys, when integrated with the AES-128 symmetric encryption algorithm, enable data encryption and decryption. This approach achieves secure data sharing within the smart grid data middle platform using combined keys under a trusted and controlled environment. Experimental results demonstrate that the trusted computing model does not impact the platform's routine operations during non-access request states, effectively intercepts unauthorized access, and maintains stable coupling even with increasing concurrent users. The combination of combined keys and the AES-128 algorithm ensures that encrypted data exhibits uniform character frequency distribution, significantly enhancing resistance to analysis and safeguarding the confidentiality and availability of shared data.
Detection of unauthorized drug residues in animal-derived foods is essential due to associated health risks. Chlorpromazine (CPZ), a toxic antipsychotic drug occasionally misused in livestock, necessitates sensitive detection techniques. This study introduces an electrochemical sensor utilizing a glassy carbon electrode modified with a nickel sulfide‑tungsten carbide (NiS/WC) nanocomposite. The composite was synthesized via hydrothermal methods and characterized using XRD, FT-IR, SEM, and XPS, confirming its structure and surface properties. Electrochemical evaluation through cyclic and differential pulse voltammetry demonstrated improved electron transfer and catalytic efficiency. The sensor achieved a broad linear detection range (0.027-2940 μM) with a low detection limit of 14.8 nM for CPZ. It showed excellent repeatability (RSD < 2%), selectivity against common interferents, and high operational stability. Application to real meat samples yielded high recovery rates, indicating strong practical potential. This sensor provides a reliable, sensitive platform for CPZ detection in food safety monitoring.
In this paper, a linear quadratic Gaussian (LQG) control system consisting of a single user and a single server is studied as a basic framework. In this system, the user transmits its data to the server for the optimal LQG control inputs, and they communicate with each other through a shared network. However, the network is public and directly transmitting private data may lead to unauthorized access by external adversaries, which raises the privacy concerns for the user. Therefore, we design a privacy scheme by the method of strategic transmission where the user transmits the distorted value at certain times based on a binary variable. We study the design of the scheme in two scenarios, and analyze the privacy and LQG control performances. We also propose the associated optimization problems and provide the optimal solutions. Finally, examples are given to illustrate the performance of the optimal privacy scheme.
To determine the proportion of Colorado firearm owners who store firearms hidden and unlocked by firearm-owner, firearm-related, and household characteristics, including the presence of children in the household. Data are from the 2023 Colorado Firearm Injury Prevention Survey, an online state-representative survey. Sociodemographic characteristics, number and type of firearms owned, and storage practices were assessed for respondents who reported owning at least one firearm (n = 432). The primary outcome was the proportion of firearm owners storing any firearm hidden and unlocked. Findings are presented as weighted percentages and 95% confidence intervals (CI). Among Colorado firearm owners in 2023, 47.2% (95% CI: 40.7, 53.9) stored at least one firearm of any type hidden and unlocked, 39.7% (95% CI: 33.3, 46.4) stored at least one handgun hidden and unlocked, and 25.7% (95% CI: 20.5, 31.8) stored at least one long gun hidden and unlocked. Among households with children present, 31.3% (95% CI: 21.6, 42.9) stored at least one firearm hidden and unlocked. Storing firearms hidden and unlocked is common among firearms owners in Colorado. Efforts to prevent firearm injuries, especially by children and adolescents, should emphasize the risks of relying solely on hiding firearms to prevent unauthorized access.
The recent explosive growth of Unmanned Aerial Vehicles (UAVs) has contributed to their high vulnerability to cyber-attacks including Denial of Service (DoS), identity impersonation and unauthorized access to data. The UAV networks have the inherent risks of centralized Intrusion Detection Systems (IDS) which pose critical privacy risks and points of failure, and therefore the decentralized and privacy-preserving learning paradigms are required. The paper presents federated learning architecture called FedDrone-Shield( Federated Learning Framework for Drone Security and Shield against Intrusions), which is used in the task of detecting UAV intrusions in the scenarios of Independent and Identically Distributed (IID) data and the assessment of several aggregation algorithms: FedAvg, FedProx, FedAdam, FedMedian, and ClusterAvg. A significant amount of experiments that were carried out on a dataset on anomaly detection of UAVs prove that FedAdam and ClusterAvg outperform other aggregation strategies by achieving test accuracies of 99.98, F1-scores of 0.9999, and impressively low loss values of 0.0009-0.0014. FedMedian has also closely competitive performance, whereas FedAvg and FedProx are slightly less accurate and slower converging. Client-level assessments also show a consistent high precision, recall and F1-score across all attack types, with weighted F1-scores between 0.9997 and 0.9999, which again shows that there is reliable detection performance amongst distributed UAV clients. These findings make FedDrone-Shield a strong and feasible bench-marking model of federated intrusion detection in UAV networks proving that adaptive aggregation approaches contribute to a substantial improvement of detection accuracy, training, and data privacy. This means that the proposed structure offers a robust basis of intrusion detection that is safe and ensures privacy in distributed UAVs.
Conventional batteries are heavy, nonconformal, and toxic, while energy harvesters offer low/variable power, rendering them unsuitable for emerging wearable, robotic, and other Internet-of-Things (IoT) devices. Here, we introduce a nontoxic, thin-film battery superior to several commercial batteries inspired by advances in water harvesting techniques. It scavenges ambient moisture to serve as its electrolyte, combining features of an energy harvester with attractive features of a battery (stable and high power). It functions in a wide range of environments and offers an open-circuit voltage of ~1.6 volts, specific capacity of ~52 milliampere-hours per gram, and specific energy of ~81 milliwatt-hours per gram. Unique pangolin skin-mimicking design imparts high flexibility, stretchability, and fill factor to the battery, ensuring conformability without compromising on capacity. Experimental and computer simulation studies reveal the distinctive materials and design features responsible for the battery's impressive performance. Examples of a wireless wearable pulse oximeter and surveillance monitor with a unique kill switch that secures it from unauthorized access demonstrate the versatility of the battery to power various IoT devices.
Internet of Things (IoT) plays a vital role in various applications, enabling device-level communication. However, challenges such as unauthorized access, latency, and data breaches require efficient and secure solutions. This paper proposes a Fog Computing-based Fault - Tolerance Clustering Protocol (FC-FTCP) integrated with lightweight cryptography and Off-the-Record (OTR) authentication to enhance data security and transmission reliability in IoT environments. The FC-FTCP protocol supports decentralized data processing and fault-tolerant routing to reduce delays and improve energy efficiency. Simulation results demonstrate that FC-FTCP achieves an efficiency ratio of 98.45%, accurate data authentication at 95.12%, reduced network overhead (21.19%), lower latency (15.13%), computational overhead of 9.5 ms, and reduced computational cost (23.12%). Compared with existing protocols such as FEA-SDTS, HLSF, DIoTA, and CMANET-IoTIF, FC-FTCP shows consistent improvements in secure data handling and resource optimization, making it suitable for low-power IoT deployments.
Anthrax remains a persistent transboundary zoonotic disease in the Republic of Kazakhstan despite long-term vaccination programs and established veterinary surveillance. This study aimed to analyze spatial and temporal patterns of anthrax outbreaks in livestock and humans and to describe their relation to environmental, climatic, and immunoprophylactic factors. Retrospective data on anthrax outbreaks in cattle, small ruminants, and humans recorded between 2015 and 2024 were analyzed using descriptive temporal trend analysis and thematic spatial mapping at the regional level. Climatic parameters, including temperature, precipitation, and soil moisture, were evaluated in relation to outbreak dynamics. Serological monitoring of cattle was conducted using the indirect hemagglutination assay to assess serological response, and non-parametric statistical methods were applied to compare regions and time periods. A total of 33 anthrax outbreaks were registered, with 93.4% occurring in cattle. Outbreaks showed pronounced seasonality, peaking in summer and early autumn, and were concentrated in historically endemic regions. Despite reported vaccination coverage exceeding 97% in several regions, outbreaks persisted, suggesting the influence of multiple factors, including environmental persistence of Bacillus anthracis spores and variability in vaccination practices, animal coverage, or environmental persistence of Bacillus anthracis spores. Serological analysis revealed regional heterogeneity in antibody levels. Human cases were temporally associated with livestock outbreaks and were primarily linked to unauthorized slaughter and handling of infected animals. These findings underscore the need for integrated risk-based surveillance combining climatic monitoring, spatial analysis, and targeted vaccination to support anthrax prevention strategies in endemic areas.
As the recent developments in digital infrastructures have led to increased risk of cyber threats. Among them, quantum technology assisted threats signal an unprecedented and urgent threat type referring to cyberattacks that utilize quantum computing and quantum mechanics to attack traditional cryptographic solutions. For example, algorithms such as Shor's offer the capability to breach RSA and ECC encryption exponentially faster than any classical-based route resulting in the potential for unauthorized access to vast amounts of data and destabilization of sensitive systems over a longer time period. More differently than traditional attacks, quantum empowered threats are uniquely dangerous in that, as quantum hardware becomes sufficiently capable or complex, they can depend on captured data to retroactively decrypt messages. Current security mechanisms that are reactive-based do not anticipate nor defend against these threats, leaving critical networks vulnerable to threats faced by both classical actors and actors capable of quantum-enabled computation. To tackle this massive and dangerous challenge, we propose a new Quantum-Safe Cyber Digital Twin (QS-CDT) architecture that is intended to purposely forecast cyber threats and dynamically orchestrate adaptive defences. QS-CDT maintains a dynamic digital twin of the live network that is always up-to-date and is protected using post-quantum algorithms that are both CRYSTALS-Kyber and Dilithium for end-to-end quantum resilience. State-of-the-art quantum inspired machine learning algorithms in the twin will simulate and consider attack vectors and emerging threats in real-time, thus enabling preventive action through the knowledge of the threats, their impacts and potential mitigation actions. Here, quantum-inspired machine learning refers to classical algorithms executed on conventional hardware that adopt mathematical principles from quantum computing, such as tensor networks and variational optimization, without requiring physical quantum processors. Unlike other digital twin approaches which center on replicating and passively monitoring the system, the proposed approach combines real-time simulation of potential threats with an automated, policy-driven reconfiguration of the network. Our initial simulation trials show a 35% improvement in latency of threat detection and a 40% improvement in adaptive response time to accommodate unpredictable responses than a less efficient, traditional reactive approach. This improves in turn the overall security posture of the network. Future work shall focus on expanding QS-CDT to large, heterogeneous networks, improve the predictive modeling of quantum potential threat scenarios, and assimilate the architecture with existing operational cyber defense networks. These findings represent an important platform for a next-generation proactive cybersecurity model in the post-quantum computing era.
Physically unclonable functions (PUFs) are a promising solution for anticounterfeiting technologies. However, their mass implementation is limited by fabrication complexity and/or special materials demands, as well as specialized authentication. To overcome these issues, a scalable and sustainable approach for fabricating optical PUF labels is proposed in this work. The approach is based on direct laser metallization (DLM) of deep eutectic solvents on flexible polyimide substrates. The laser-induced reduction of copper precursors produces microstructures made by specially designed templates with stochastic edge morphologies serving as unique unclonable identifiers. A computer vision-based authentication algorithm enables robust recognition, achieving an encoding capacity exceeding 10465. The created labels can be authenticated using standard microscopes or consumer smartphones with macro lenses. Additionally, created labels were tested for stability and unauthorized copying. The replication attempts using 1200 dpi laser printing reproduced macroscopic outlines but failed to mimic microscopic features, confirming their unclonability. The fabricated labels remain functional after exposure to running water and heating up to 200°C, demonstrating excellent environmental durability. Due to its flexibility, scalability, and low-cost eco-friendly processing, the proposed label design is well suited for integration into flexible and wearable microelectronic systems, providing a practical solution to next-generation anticounterfeit technologies.
Cosmetic products are an integral part of daily life. However, although the regulatory framework for such products appears strict, customer safety and product quality are not guaranteed. A large proportion of consumers prefer using cosmetic products that contain natural substances to avoid exposure of their skin to chemicals. Nevertheless, some of the products marketed to meet this need are not natural or even safe. This article presents an overview of reports on cosmetic products declared to be of natural origin in the European Union Safety Gate system between 2005 and 2023. Many reported cosmetic products posed a chemical risk by containing unauthorized substances (64%) or exceedance of authorized limits (14%). Butylphenyl methylpropional (also known as lilial) was the dominant ingredient present in several product categories. Microbial contamination was also detected (12%), but at a markedly lower rate compared to that recorded for the presence of chemicals (79%). Use of the term "natural" to describe cosmetic products can be misleading because it does not always comply with the criteria for natural and organic cosmetic components. Importantly, such cosmetic products could pose a serious risk to consumers due to hidden unsafe substances. Hence, implementation of appropriate measures is necessary to ensure consumer safety.
Secure image transfer is a critical requirement in telemedicine and Picture Archiving and Communication Systems (PACS), where diagnostic integrity and patient confidentiality must be simultaneously ensured. High-resolution medical images, especially Magnetic Resonance Imaging (MRI), are often transferred over bandwidth-limited networks without impairing diagnostic quality or confidentiality against unauthorized access, manipulation, and replay. Existing approaches generally rely on a cascaded pipeline where image compression, encryption, and verification are considered as separate, independent operations. These distributed designs are prone to cryptographic key management issues, suffer from non-deterministic error control, lack meaningful coupling between image identity and cryptographic state, and provide no verifiable provenance or post-quantum key migration support. In this paper, we propose a DICOM-compliant system that integrates [Formula: see text]-regularized near-lossless image compression, hyperchaotic permutation diffusion encryption, post-quantum secure key establishment, and blockchain-based provenance verification as a unified operation. JPEG 2000 is used for primary image compression, and a residual refinement layer is introduced to limit residual correction amplitudes by [Formula: see text]-regularization to retain diagnostically significant information. Hyperchaotic encryption is performed with a symmetric image key generated using the NIST-standard post-quantum Key Encapsulation Mechanism (ML-KEM) and extracted using the HMAC-based Key Derivation Function (HKDF), cryptographically linked to DICOM image metadata. Message integrity is ensured using a keyed-hash message authentication code (HMAC), while a permissioned blockchain records only non-sensitive cryptographic metadata to enable verifiable provenance. Experimental results on clinical MRI and susceptibility-weighted imaging datasets demonstrate high reconstruction quality (PSNR: 46-51 dB, SSIM: 0.92-0.97), compression ratios ranging from 7.14:1 to 23.03:1 depending on modality and [Formula: see text], and strong cryptographic properties (NPCR: 99.6%, UACI: 33.46%, entropy: 7.9993). The evaluation focuses on MRI-based modalities, reflecting the intended scope of this study.