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

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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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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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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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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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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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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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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

“…Additionally, the communication protocol of SRFLM provides strong privacy guarantees using differential privacy. We extensively evaluate SRFLM across multiple cross-vendor diagnostic imaging tasks. …”
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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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PLASMA-Privacy-Preserved Lightweight and Secure Multi-level Authentication scheme for IoMT-based smart healthcare by Hegde Manjunath, M. Karthik, Hegde Varun, Rao Rohini R., Mantoor Vinayak M., Bhat Radhakrishna

“…PLASMA’s security and key privacy are validated through the Real-Or-Random (ROR) model and simulated using the widely accepted Scyther tool. …”
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Leveraging Complex-Valued Federated Learning for Accurate and Privacy-Respectful Threat Detection Based on Millimeter-Wave Imaging by Hadi Mahdipour, Jaime Laviada, Fernando Las-Heras Andres, Mehdi Sookhak

“…This approach provides a highly accurate and privacy-preserving solution for CO detection using high-resolution MMW radar. …”
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Privacy-Preserving Federated Learning in Healthcare, E-Commerce, and Finance: A Taxonomy of Security Threats and Mitigation Strategies by Kumar Rahul, Shieh Chin-Shiuh, Chakrabarti Prasun, Kumar Ashok, Moolchandani Jhankar, Sinha Raj

“…Federated Learning (FL) transformed decentralized machine learning by allowing joint model training without mutually sharing raw data, hence being especially useful in privacy-sensitive applications like healthcare, e-commerce, and finance. …”
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