Frontiers in Big Data最新文献

{"title":"Data field theory: a geometric framework for learning on Riemannian manifolds with synthetic validation and limitation analysis.","authors":"Mohammadreza Nehzati","doi":"10.3389/fdata.2026.1752468","DOIUrl":"10.3389/fdata.2026.1752468","url":null,"abstract":"<p><strong>Introduction: </strong>Conventional machine learning treats learning as parameter optimization, lacking a first-principles framework for phenomena like criticality, generalization, and causal structure. We introduce Data Field Theory (DFT), a mathematical framework modelling learning as the evolution of a data field governed by stochastic partial differential equations on Riemannian manifolds. This work aims to validate DFT's core predictions in settings where its geometric assumptions hold, while honestly assessing its empirical limitations.</p><p><strong>Methods: </strong>We formulate learning as a field <math><mi>φ</mi> <mo>:</mo> <mi>M</mi> <mo>×</mo> <msub><mrow><mi>ℝ</mi></mrow> <mrow><mo>≥</mo> <mn>0</mn></mrow> </msub> <mo>→</mo> <msup><mrow><mi>ℝ</mi></mrow> <mrow><mi>k</mi></mrow> </msup> </math> evolving on a spherical manifold. To test DFT, we implement a hierarchical classification task using synthetic data drawn from von Mises-Fisher distributions, ensuring match with the manifold geometry. We derive four key predictions: (1) critical exponents near concept formation, (2) a spectral robustness law linking Eigen gaps to out-of-distribution (OOD) error, (3) finite-speed causal propagation from hyperbolic regularization, and (4) approximate rotational equivariance via a Ward identity. We also conduct a preliminary real-data experiment projecting MNIST digits onto the sphere.</p><p><strong>Results: </strong>Synthetic experiments validate all four predictions: (1) Correlation length diverges as <math><mi>ξ</mi> <mrow><mo>(</mo> <mrow><mi>t</mi></mrow> <mo>)</mo></mrow> <mo>~</mo> <mo>|</mo> <mi>t</mi> <mo>-</mo> <msub><mrow><mi>t</mi></mrow> <mrow><mi>c</mi></mrow> </msub> <msup><mrow><mo>|</mo></mrow> <mrow><mo>-</mo> <mi>ν</mi></mrow> </msup> </math> with ν = 0.63 ± 0.04, accompanied by 1/f fluctuations; (2) OOD generalization error scales as <math> <msub><mrow><mi>ϵ</mi></mrow> <mrow><mtext>OOD</mtext></mrow> </msub> <mo>∝</mo> <msubsup><mrow><mi>m</mi></mrow> <mrow><mstyle><mtext>gap</mtext></mstyle> </mrow> <mrow><mo>-</mo> <mn>2</mn></mrow> </msubsup> </math> (ρ = -0.78, <i>p</i> < 10<sup>-6</sup>); (3) Causal propagation speed <i>c</i> <sub>eff</sub> = 0.98 ± 0.03 (theory maximum <i>c</i> <sub>max</sub> = 1.0) under hyperbolic regularization; and (4) Ward identity residual <i>R</i> = 0.0032 ± 0.0008 converging as <i>R</i>∝<i>h</i> <sup>1.02</sup>. However, on real-world MNIST-sphere data, DFT achieves only 15.7% accuracy versus 51.7% for k-NN, revealing critical limitations.</p><p><strong>Discussion: </strong>DFT successfully predicts emergent phenomena criticality, spectral robustness, bounded causality, and approximate equivariance under ideal geometric conditions, supporting its theoretical validity. The poor real-data performance highlights key gaps: the current framework lacks adaptive metric learning, noise robustness, and hierarchical feature extraction present in real images. These results establish DFT as a pri","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1752468"},"PeriodicalIF":3.8,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13293909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Explainable gradient convolutional vector fuzzy pattern analysis based on ensemble model for facial expression recognition.","authors":"Lakshmi Sarvani Videla, Babu Reddy Mukamalla","doi":"10.3389/fdata.2026.1883246","DOIUrl":"10.3389/fdata.2026.1883246","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fdata.2026.1807184.].</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1883246"},"PeriodicalIF":3.8,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13292257/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148334183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When uncertainty guides learning: a highly effective approach to kidney disease classification in CT imaging.","authors":"Muslima Akter, Fahmid Al Farid, Md Yousuf Ahmad, Md Azad Hossain Raju, Sowad Rahman, Jia Uddin, Hezerul Bin Abdul Karim","doi":"10.3389/fdata.2026.1825213","DOIUrl":"10.3389/fdata.2026.1825213","url":null,"abstract":"<p><p>The high cost of expert annotations significantly hinders the advancement of deep learning models for clinical medical imaging. This work introduces an efficient entropy-based active learning framework that achieves outstanding classification performance for renal abnormalities (Normal, Cyst, Stone, Tumor) in CT scans while requiring only a minimal amount of labeled data. The dataset comprises 12,446 CT slices split 70/15/15 into training (8,716), validation (1,865), and test (1,865) partitions via stratified sampling. Starting with only 200 randomly selected images and employing predictive entropy for uncertainty sampling on a pretrained ResNet-50 backbone, the proposed method attains 99.71% ± 0.25% mean test accuracy (95% CI: [99.30, 99.94]) across five independent runs after just six query cycles on the standard 12,446-image CT kidney dataset. Our method uses only 2,000 labeled training images, representing 22.9% of the 8,716-image training partition (a 77.1% reduction in required annotations relative to full supervision of the training set). This performance matches or exceeds prior fully supervised methods trained on the complete labeled training partition while demonstrating substantially improved sample efficiency, particularly in early annotation cycles where entropy-guided selection converges significantly faster than random sampling. Statistical testing across five repeated runs confirms that results are stable (Shapiro-Wilk <i>p</i> = 0.148). The framework exhibits exceptional sample efficiency as described by an empirically fitted power-law curve with a fitted exponent of 1.2, and empirically observed uncertainty decay with a rate of 0.92. These results offer both practical insights into annotation efficiency and substantial application value in the medical imaging domain.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1825213"},"PeriodicalIF":3.8,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13286790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148320727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Democratizing cloud data lake analytics: natural language access to Apache Iceberg via LLM agents.","authors":"Vipin Kataria, Nitin Kumar","doi":"10.3389/fdata.2026.1785710","DOIUrl":"https://doi.org/10.3389/fdata.2026.1785710","url":null,"abstract":"<p><p>Business analysts and non-technical users need insights from enterprise data lakes but lack SQL expertise to query them directly. While large language models (LLMs) can translate natural language to SQL, existing text-to-SQL approaches face critical limitations: severe SQL injection vulnerabilities, inability to leverage data-lake-specific features like time-travel queries, and inconsistent metric definitions across organizations. We present the LangChain Iceberg Toolkit, enabling users to query Apache Iceberg data lakes through natural language conversations with LLM agents, no SQL knowledge required. Users ask questions in plain English (e.g., \"What was revenue last quarter?\"), and the system automatically: (1) interprets intent using LLMs, (2) selects appropriate tools from a YAML-based semantic layer mapping business terms to data structures, (3) executes queries through a hybrid architecture combining PyIceberg's type-safe API (for security) with DuckDB's SQL engine (for complex analytics), and (4) returns formatted answers with business context. Our evaluation demonstrates 100% success across 100 systematically designed queries leveraging semantic layer integration for consistent metric definitions. Critically, in direct comparison against a schema-aware text-to-SQL baseline on the same query set, our system achieves a 33 percentage-point accuracy improvement (100% vs. 67%) while reducing SQL injection attack success rate from the 99% reported in prior text-to-SQL research to 0% across both execution paths. End-to-end query latency averages 2.6 seconds on 15.1M records, with partition pruning eliminating 90%+ of scanned data files. The hybrid execution architecture prevents SQL injection vulnerabilities through type-safe query construction for simple queries and controlled, pre-validated SQL execution for complex analytics. Users receive data insights through conversational interfaces without writing SQL, understanding schemas, or knowing technical implementation details. We provide a production-ready, open-source implementation demonstrating practical viability for democratizing enterprise data access.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1785710"},"PeriodicalIF":3.8,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13284880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148310470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TCMB: cross-model multi-level cross-attention network with Taylor-based loss for multimodal fake news detection.","authors":"Santosh Kumar Banbhrani","doi":"10.3389/fdata.2026.1796969","DOIUrl":"10.3389/fdata.2026.1796969","url":null,"abstract":"<p><strong>Introduction: </strong>The rapid spread of misinformation across social media platforms, websites, and online communication channels has made fake news detection a critical task in the digital era. Although various computational approaches have been developed to identify fake news, many existing methods suffer from limitations such as biased training datasets and high rates of false positives and false negatives. To address these challenges, this study proposes a Multimodal Cross Attention Network with Taylor-based Cross Entropy Mean Bias (MMCN_TCMB) model for detecting multimodal fake news.</p><p><strong>Methods: </strong>The proposed approach utilizes multimodal inputs consisting of textual and visual content obtained from fake news datasets. The textual information in news posts is first tokenized using Bidirectional Encoder Representations from Transformers (BERT). Feature extraction is then performed using Word2Vec and Term Frequency-Inverse Gravity Moment (TF-IGM). Simultaneously, images associated with news posts undergo preprocessing through Contrast Limited Adaptive Histogram Equalization and Histogram Equalization (CLAHE-HE), followed by feature extraction using ResNet. The extracted textual and visual features are combined and processed through the MMCN framework. The learning mechanism of the network is enhanced using the Taylor-based Cross Entropy Mean Bias (TCMB) loss function to improve classification performance.</p><p><strong>Results: </strong>Experimental results demonstrate that the proposed MMCN_TCMB model achieves superior performance in multimodal fake news detection. The model attains a recall of 97.988%, precision of 96.223%, F1-score of 97.098%, and overall accuracy of 97.436%, outperforming existing methods.</p><p><strong>Discussion: </strong>The findings indicate that integrating multimodal feature extraction with cross-attention mechanisms and the TCMB loss function significantly enhances the reliability and accuracy of fake news detection. The proposed framework effectively captures both textual and visual inconsistencies, making it a promising approach for combating misinformation in modern digital platforms.<b>The code is available on:</b> https://github.com/banbhrani84/MMCN_TCMB-Fake-News-.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1796969"},"PeriodicalIF":3.8,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13279328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148297356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable intrusion detection for IoT: a CNN-BiLSTM permutation importance framework for deep feature selection.","authors":"Ibrahim Al-Shibly, Llorenç Burgas, Joaquim Massana","doi":"10.3389/fdata.2026.1813265","DOIUrl":"10.3389/fdata.2026.1813265","url":null,"abstract":"<p><p>Industrial intrusion detection systems (IDS) in Industrial Internet of Things (IIoT) environments have to address the problem of handling multi-feature temporally correlated network traffic and dynamic changes in attack patterns. Traditional filter-based feature selection methods, like Mutual Information (MI), only consider individual feature performance and may not be effective in dealing with non-linear feature dependencies. This may degrade detection performance, especially in class-imbalanced problems. To mitigate such challenges, this paper proposes a deep feature selection (DFS) framework that utilizes a hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) model. The proposed framework assesses the importance of native features using permutation importance. In the proposed framework, the CNN model detects local features in the data, whereas the BiLSTM model detects bidirectional temporal features in the data. The importance of features is computed by assessing the performance degradation of the model using time-aware perturbations on individual features. These identified features that are most relevant are then used to train lightweight traditional machine learning models like decision tree, K-nearest neighbor (KNN), logistic regression, naïve Bayes, and random forest. This makes it easy to deploy in resource-constrained IIoT environments. The approach is tested on the CIC IIoT 2025 dataset. From the experimental results, it is clear that the CNN-BiLSTM DFS framework improves recall and F1-score compared to other feature selection approaches like MI. This is especially true in imbalanced settings. The decoupling of feature selection from offline and edge-side inference provides a balance between detection accuracy, robustness, and deployability in real-world IIoT settings.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1813265"},"PeriodicalIF":3.8,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13236541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Definitional ambiguity in cognitive warfare: a critical and systematic conceptual review through ideal-type analysis.","authors":"Per-Erik Nilsson, Andreas Haga, Kristina Hellström","doi":"10.3389/fdata.2026.1762571","DOIUrl":"10.3389/fdata.2026.1762571","url":null,"abstract":"<p><p>Cognitive warfare is a relatively new concept in both military and academic discourse. The article's purpose is to advance conceptual clarity regarding cognitive warfare and to support future policy-oriented and academic research that strengthens the field's conceptual and methodological foundations, understood here as the broader domain of communication and defense studies concerned with informational and cognitive forms of contestation. This article examines how the notion is conceptualized within the emerging body of research, drawing on a systematic literature review. With support from LLM-assisted analysis, the study employs an exploratory methodology to identify both conceptual commonalities and points of divergence. The review indicates that cognitive warfare remains an underdeveloped research field, characterized by broad assumptions and limited scientific rigor. While the concept may represent a reframing of long-standing practices, it may also serve a political function by drawing renewed attention to forms of influence and conflict that have been overshadowed in recent decades. The article concludes by outlining avenues for future interdisciplinary research, emphasizing the need for conceptual clarity, empirical operationalization, and a more nuanced understanding of how adversaries themselves articulate and employ cognitive warfare.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1762571"},"PeriodicalIF":3.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13218914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying energy and accuracy trade-offs of federated learning on wearable health devices.","authors":"Rupaak S, Ganesh Khekare, Yash Kumar, Gaurav Soni","doi":"10.3389/fdata.2026.1769948","DOIUrl":"10.3389/fdata.2026.1769948","url":null,"abstract":"<p><p>The rapid development of wearable health tools has made it possible to continuously monitor physiological conditions for preventive care. However, stringent privacy laws, including HIPAA and GDPR, require decentralized methods such as federated learning (FL) to safeguard personal patient information. Nonetheless, empirical profiling in this paper finds that typical FL implementations are plagued by a serious performance trilemma; a naive federated model attains a 35.3 percent energy savings (3.84 vs. 5.93 kJ in the centralized models), but at the cost of a disastrous performance penalty of 13.87 percentage points (84.94 vs. 98.81 percent in centralized models). The failure in research is largely due to the on-device computational load of 4.24 MFLOPs per training sample, resulting in a \"straggler\" bottleneck that increases the total training duration to 1,066.26 s, almost 70 times longer than centralized training. As a result, the introduction of the hybrid hierarchical federated split learning (H-FedSL) architecture helps in strategically splitting the neural network at a cut layer to divide the workload between wearable and nearby edge servers. The methodology provides a new framework that offloads the heavy and deep-layer computations to the edge server, leaving the shallow feature extraction to the point of operation, and sends only privacy-sensitive abstractions of the smashed data, rather than raw signals. The integration of asynchronous protocols will help manage device heterogeneity and resource-aware client selection, thereby achieving the aim of H-FedSL to restore the gold-standard accuracy of 98.81% with the state-of-the-art 35.3% energy efficiency of the federated model. Thus, a technically and economically feasible pathway will be provided for deploying medical-grade AI on resource-constrained Internet of Medical Things (IoMT) devices.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1769948"},"PeriodicalIF":3.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13220697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of statistical methods and artificial intelligence in inventory management for manufacturing industries: a systematic literature review.","authors":"Arvia Dwi Royani, Mahfud Sholihin, Dewi Dewi, Novika Novika, Annisa Sorayya, Wahyu Nur Hanifah, Rizki Ramadhani Arif Trilana, Paolina Buton","doi":"10.3389/fdata.2026.1799073","DOIUrl":"10.3389/fdata.2026.1799073","url":null,"abstract":"<p><p>Inventory management is a critical business process that affects the operational efficiency and competitiveness of manufacturing companies. Inaccurate inventory decisions can result in significant financial losses for companies. Demand variability poses a challenge in determining inventory levels, requiring more sophisticated, flexible forecasting methods. This study was conducted to examine the roles of statistical methods and Artificial Intelligence (AI) in inventory decision-making in the manufacturing industry, analyze the conditions under which each method is suitable, and evaluate the potential of a hybrid approach integrating statistical methods and AI. This study uses the Systematic Literature Review method with the PRISMA 2020 framework to ensure research transparency and accuracy. This study identifies articles from reputable databases indexed in Scopus. The findings show a significant shift in inventory management research. In the last decade, AI technology has dominated the literature at 62.5%, while statistical methods account for 25%, and hybrid methods have begun to emerge but remain limited to 12.5%. Based on the review of selected papers, statistical methods have proven to remain effective for consistent historical data and stable demand patterns. Conversely, in dynamic operational environments with large-scale data and complex nonlinear patterns, AI technology is superior. This study also found that the hybrid approach has great potential to balance accuracy, interpretability, and decision support, although the relevant literature remains limited. The implementation of technology in the manufacturing industry faces several obstacles, including limited data quality, a skills gap in technology, and the black-box nature of complex AI. This review provides a systematic and critical synthesis of methodological patterns and operational fit in the use of statistical, AI, and hybrid methods for manufacturing inventory management. Future research is recommended to focus on the development of interpretable AI, modular hybrid frameworks, and the use of real industry data to ensure that academic innovations can be applied in the manufacturing industry.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1799073"},"PeriodicalIF":3.8,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13218985/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KATENA: a verifiable governance architecture for encrypted cloud storage systems.","authors":"Jesús F Rodríguez-Aragón, Carolina Zato, Francisco Pinto-Santos, Lorena Sánchez-Pravos","doi":"10.3389/fdata.2026.1829960","DOIUrl":"https://doi.org/10.3389/fdata.2026.1829960","url":null,"abstract":"<p><p>Modern data-intensive infrastructures increasingly rely on cloud storage and client-controlled encryption to protect the confidentiality of outsourced information. However, while encryption prevents providers from accessing plaintext data, governance operations such as sharing, revocation, and policy updates typically remain opaque to users and auditors. This creates a structural gap between strong data confidentiality and verifiable governance in cloud environments that manage large volumes of sensitive information. This paper introduces KATENA (Key Architecture for Trustworthy Encrypted Networked Archives), an architectural model that enables client-verifiable governance in encrypted cloud storage systems. The proposed approach combines hierarchical key orchestration, transparency-based governance logging, and cryptographically verifiable governance artifacts so that clients can independently validate governance events without relying on provider-side trust. By integrating accountability mechanisms directly into encrypted storage architectures, the work provides a governance-by-design framework that bridges client-controlled encryption with verifiable data governance in modern data-intensive cloud systems.</p>","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1829960"},"PeriodicalIF":2.4,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13218950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}