A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications by Dileep Kumar Murala, K. Vara Prasada Rao, Veera Ankalu Vuyyuru, Beakal Gizachew Assefa

“…However, despite their potential, the practical adoption of these technologies faces critical challenges, particularly concerning data privacy and security. AI models, especially in distributed environments, may inadvertently retain and leak sensitive training data, exposing users to privacy risks in the event of malicious attacks. …”
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A Privacy Preserving Attribute-Based Access Control Model for the Tokenization of Mineral Resources via Blockchain by Padmini Nemala, Ben Chen, Hui Cui

“…A comparative analysis between ABAC and RBAC highlights how ABAC provides greater flexibility, security, and privacy for mining operations. By introducing ABAC in blockchain-based mineral reserve tokenization, this paper contributes to a more efficient and secure way of managing data access in the mining industry, ensuring that only authorized stakeholders can interact with tokenized mineral assets.…”
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Machine learning: enhanced dynamic clustering for privacy preservation and malicious node detection in industrial internet of things by Nabeela Hasan, Saima Saleem, Mudassir Khan, Abdulatif Alabdultif, Mohammad Mazhar Nezami, Mansaf Alam

“…By integrating adaptive clustering via the LEACH protocol, secure key distribution through Public Key Generators (PKGs), and private data aggregation using the Location Privacy Tree (LPT) and Chinese Remainder Theorem (CRT), the framework addresses both communication efficiency and data confidentiality. …”
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Vehicle Trajectory Generation Based on Generation Adversarial Network by Zhonghe He, Renchi Shao, Sijia Xiang

“…In this paper, the vehicle travel time of sample trajectory data and virtual trajectory data is used as indicators for statistical analysis. …”
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Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks by Nurettin Selcuk Senol, Mohamed Baza, Amar Rasheed, Maazen Alsabaan

“…Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data. …”
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Privacy–preserving dementia classification from EEG via hybrid–fusion EEGNetv4 and federated learning by Muhammad Umair, Muhammad Shahbaz Khan, Muhammad Hanif, Wad Ghaban, Ibtehal Nafea, Sultan Noman Qasem, Sultan Noman Qasem, Faisal Saeed

“…Moreover, FL using FedAvg is implemented across five stratified clients, achieving 96.9% accuracy on the hybrid fused EEGNetV4 model while preserving data privacy. …”
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Quality and Privacy Policy Compliance of Mental Health Care Apps in China: Cross-Sectional Evaluation Study by Xinying Lin, Xingxing Wu, Ziping Zhu, Danting Chen, Hong Li, Rong Lin

“…Each app was assessed for general characteristics, professional context, functionality, quality, and privacy policy compliance using a previously published privacy policy compliance scale and the MARS. …”
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A Privacy-Preserving Polymorphic Heterogeneous Security Architecture for Cloud–Edge Collaboration Industrial Control Systems by Yukun Niu, Xiaopeng Han, Chuan He, Yunfan Wang, Zhigang Cao, Ding Zhou

“…Comprehensive evaluations using natural gas pipeline pressure control and smart grid voltage control systems demonstrate superior performance: the proposed method achieves 100% system availability compared to 62.57% for static redundancy and 86.53% for moving target defense, maintains 99.98% availability even under common-mode attacks (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula> probability), and consistently outperforms moving target defense methods integrated with state-of-the-art detection mechanisms (99.7790% and 99.6735% average availability when false data deviations from true values are 5% and 3%, respectively) across different attack detection scenarios, validating its effectiveness in defending against availability attacks and privacy leakage threats in cloud–edge collaboration environments.…”
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Neutrosophic Logic for Secure Hand-Based Biometrics: Quantifying Privacy-Security Tradeoffs in Remote Authentication Systems by A. A. Salama, Abdelnasser Mohamed, Huda E. Khalid, Ahmed K. Essa, Doaa E. Mossa

“…To address this gap, we propose a vulnerability assessment framework grounded in neutrosophic logic [11], which evaluates system robustness through truth (T), indeterminacy (I), and falsity (F) membership functions, and this approach quantifies the trade-offs between security and privacy, revealing that hand-based biometrics achieve 92% security effectiveness (T = 0.8) while retaining an 18% uncertainty factor ( 𝐼 = 0.4 ) concerning potential vulnerabilities, and added analysis further identifies deficiencies in template protection (F = 0.2) and data transmission protocols, and we, proposed framework advances us the evaluation of biometric systems by using integrating neutrosophic uncertainty modeling, and it provides actionable insights for designing secure remote authentication architectures. …”
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A New Distributed User-Demand-Driven Location Privacy Protection Scheme for Mobile Communication Network by Jin Wang, Hao Wu, Yudian Liu

“…While obtaining convenient services, the exploitation of mass location data is inevitably leading to a serious concern about location privacy security. …”
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Applications of blockchain technology in long-term care: use cases, potentials, and barriers by Beate Steurer, Birgit Trukeschitz, Ulrike Schneider

“…Abstract Background Digitalization in long-term care (LTC) is being discussed as a means of easing pressures on care service delivery, optimizing care coordination and planning processes, and improving service quality. With its advanced data protection and process automation features, blockchain technology could help solve data security and privacy issues, especially for the implementation of assistive technologies. …”
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Teachers’ digital literacy competencies according to their age status and their level of use in classes by Gökbulut Burak, Yeniasır Mustafa, Gökbulut Özlem Dağlı

“…Digital literacy means that individuals know how to properly use digital platforms/tools both for their personal development and for solving everyday problems, mastering information technologies and being aware of issues such as legal responsibility, data protection, security and ethics. …”
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The Use of ChatGPT in English Language Learning: A Systematic Literature Review by Nur Hidayati, Erna Andriyanti, Novasa Adiyani

“…Following PRISMA guidelines, this review analyzes data from Scopus, ERIC, and Google Scholar using thematic analysis to identify key themes. …”
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Ethical and legal considerations in healthcare AI: innovation and policy for safe and fair use by Tuan Pham

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Artificial Intelligence and Politics: Legal Dilemmas and Risks to Democracy by Daniele Battista, Gabriele Uva

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Artificial Intelligence and Politics: Legal Dilemmas and Risks to Democracy by Daniele Battista, Gabriele Uva

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Dynamic selectout and voting-based federated learning for enhanced medical image analysis by Saeed Iqbal, Adnan N Qureshi, Musaed Alhussein, Khursheed Aurangzeb, Atif Mahmood, Saaidal Razalli Bin Azzuhri

“…This strategy has a lot of potential to improve FL across a range of applications, especially those that prioritize data privacy, diversity, and performance.…”
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The use of micromobility riding simulators in road safety research: a systematic review by Charitha Dias, Qinaat Hussain, Muhammad Abdullah

“…Collecting real-world and field data involves numerous challenges due to ethical, privacy, and safety concerns. …”
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Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter by Andrew Lowy, Meisam Razaviyayn

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Hybrid Optimization Machine Learning Framework for Enhancing Trust and Security in Cloud Network by Himani Saini, Gopal Singh, Amrinder Kaur, Sunil Saini, Niyaz Ahmad Wani, Vikram Chopra, Zahid Akhtar, Shahid Ahmad Bhat

“…The rapidly evolving field of cloud-based data sharing faces critical challenges in ensuring comprehensive privacy protection and trust for both data producers and seekers. …”
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