Communication-efficient and privacy-preserving federated learning for medical image classification in multi-institutional edge computing by Nithin Melala Eshwarappa, Hojjat Baghban, Ching-Hsien Hsu, Po-Yen Hsu, Ren-Hung Hwang, Mu-Yen Chen

“…However, the centralized learning (CL) method is problematic in terms of data privacy because of the constraint of transferring substantial volumes of data to a central server. …”
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A decade of research on genetic privacy: the findings of the GetPreCiSe Center at Vanderbilt University by Christopher Slobogin, Karli Tellis, Ellen Wright Clayton, Ellen Wright Clayton, Ellen Wright Clayton, Ellen Wright Clayton, Jay Clayton, Ayden Eilmus, Bradley A. Malin, Bradley A. Malin, Bradley A. Malin

“…While this research shows that the risk of unauthorized re-identification is often over-stated, it also identifies possible ways privacy can be compromised. Several technical and legal methods for reducing privacy risks are described, most of which focus not on collection of the data, but rather on regulating data security, access, and use once it is collected. …”
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Theme-Rheme Analysis in Probing Google’s Coercion Trespassing User’s Privacy; A Forensic Linguistics Research by Sabtra Lesmana, Sawirman, Fajri Usman

“…It represents the clause analysis through the use of theme-rheme in probing the user's privacy violations in Google's user privacy policy agreement: a forensic linguistics research. …”
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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

“…Federated learning (FL) is a promising technique for training machine learning models on distributed, privacy-aware datasets. Nevertheless, FL faces difficulties with agent/client participation, model performance, and the heterogeneous nature of networked data sources when it comes to distributed healthcare systems. …”
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Trust-aware privacy-preserving QoS prediction with graph neural collaborative filtering for internet of things services by Weiwei Wang, Wenping Ma, Kun Yan

“…However, issues related to information credibility, user data privacy, and prediction accuracy in QoS prediction for IoT services have become significant challenges in current research. …”
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Federated XAI IDS: An Explainable and Safeguarding Privacy Approach to Detect Intrusion Combining Federated Learning and SHAP by Kazi Fatema, Samrat Kumar Dey, Mehrin Anannya, Risala Tasin Khan, Mohammad Mamunur Rashid, Chunhua Su, Rashed Mazumder

“…This act of sharing the original network data with another server can worsen the current arrangement of protecting privacy within the network. …”
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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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Losing control: The effects of social media fatigue, privacy concerns and psychological reactance on social media advertising by Nathalie Nicola

“…This caused users to feel negatively after using SMP. Ad avoidance is caused by significant privacy concerns when encountering personalized ads, such as suspicion as to how data is used, the feeling of being watched and listened to, as well as annoyance at the irrelevance and repetitiveness of SMA. …”
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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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Ethical-legal implications of AI-powered healthcare in critical perspective by Mohammad Nasir, Kaif Siddiqui, Samreen Ahmed

“…This requires an appraisal of AI use from a legal and ethical perspective. A review of the existing literature on AI in healthcare available on PubMed, Oxford Academic and Scopus revealed several common concerns regarding the relationship between AI, ethics, and healthcare—(i) the question of data: the choices inherent in collection, analysis, interpretation, and deployment of data inputted to and outputted by AI systems; (ii) the challenges to traditional patient-doctor relationships and long-held assumptions about privacy, identity and autonomy, as well as to the functioning of healthcare institutions. …”
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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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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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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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It’s Time to Relax: The Critical Importance of Digital Mental Health Products in the Context of Surveillance Capitalism by Sena Partal, Sasha Smirnova

“…We want to move beyond the personal data privacy debate and tackle other potential issues – what does this data sharing mean in terms of a shift in collective psychology and ideologies? …”
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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

“…Although mental health care apps can enhance outcomes, their handling of highly sensitive personal data poses significant privacy risks. …”
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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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GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence by Mohd Anjum, Ashit Kumar Dutta, Ali Elrashidi, Sana Shahab, Asma Aldrees, Zaffar Ahmed Shaikh, Abeer Aljohani

“…Abstract The Internet of Things (IoT) consists of physical objects and devices embedded with network connectivity, software, and sensors to collect and transmit data. The development of the Internet of Things (IoT) has led to various security and privacy issues, including distributed denial-of-service (DDoS) attacks. …”
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