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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Ethics of Artificial Intelligence and Robotics: Key Issues and Modern Ways to Solve Them by N. Yadav

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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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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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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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Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter by Andrew Lowy, Meisam Razaviyayn

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Exploring the future of privacy-preserving heart disease prediction: a fully homomorphic encryption-driven logistic regression approach by Vankamamidi S. Naresh, Sivaranjani Reddi

“…Abstract Homomorphic Encryption (HE) offers a revolutionary cryptographic approach to safeguarding privacy in machine learning (ML), especially in processing sensitive healthcare data. …”
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Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning by G Muni Nagamani, Chanumolu Kiran Kumar

“…This system processes over 100,000 messages per second with less than 50 ms of inference latency to enable the prompting of clinical responses. We use Federated Learning combined with Differential Privacy to solve our data security and privacy tasks. …”
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Assessing the Uses, Benefits, and Limitations of Digital Technologies Used by Health Professionals in Supporting Obesity and Mental Health Communication: Scoping Review by Amanda Kearns, Anne Moorhead, Maurice Mulvenna, Raymond Bond

“…MethodsA comprehensive scoping review, which followed a validated methodology, analyzed studies published between 2013 and 2023 across 8 databases. The data extraction focused on HPs’ use of communication technologies, intervention types, biopsychosocial considerations, and perceptions of technology use. …”
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Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning by Hao Wang, Xiaoyu Zhang, Xuebin Ren, Zheng Zhang, Shusen Yang, Chunfeng Lian, Jianhua Ma, Dong Zeng

“…Abstract Federated Learning (FL) is a distributed framework that enables collaborative training of a server model across medical data vendors while preserving data privacy. However, conventional FL faces two key challenges: substantial data heterogeneity among vendors and limited flexibility from a fixed server, leading to suboptimal performance in diagnostic-imaging tasks. …”
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Old MacDonald and His Hackable Smartphone Application: A Security and Privacy Analysis of Android Agriculture Applications by Logan Mears, Rachel Donner, George Grispos

“…Despite the widespread use of smartphones among farmers increasing from 77% in 2021 to 82% in 2023, minimal research has examined the security and privacy implications of agricultural smartphone applications, potentially used on these devices. …”
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Security and privacy in online teaching during the COVID-19 pandemic: experiences and concerns of academics in Saudi higher education by Basmah Almekhled, Helen Petrie

“…The findings provide several key insights: Key aspects of online privacy and security for Saudi HEI academics: Saudi HEI academic’s notion of online privacy and security is about the protection of personal data, preventing unauthorized access to data and ensuring the confidentiality and integrity of data. …”
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Biometrics, e-Identity, and the Balance between Security and Privacy: Case Study of the Passenger Name Record (PNR) System by G. Nouskalis

“…The implementation of biometrics entails either the establishment of an identity or tracing a person's identity. Biometric passport data (e.g., irises, fingers, faces) can be used in order to verify a passenger's identity. …”
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Investigating the Relationship between Social Skills and Social Acceptance with Academic Performance, Considering the Mediating Role of Ethical Privacy by Taghreed Radhi Abdol Mohsin, Hassan Ghalavandi

“…Four standard questionnaires of social skills, social acceptance, respect for moral privacy, and academic performance were used for gathering data. …”
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Do Cookie Banners Respect My Browsing Privacy? Measuring the Effectiveness of Cookie Rejection for Limiting Behavioral Advertising by Mateo Ormeno, Ha Dao, Valeria Herskovic, Kensuke Fukuda

“…Online behavioral advertising (OBA) is a method within digital advertising that exploits web users&#x2019; interests to tailor ads. Its use has raised privacy concerns among researchers, regulators, and the media, emphasizing the need for a reliable mechanism to measure its prevalence. …”
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