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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Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption by Kamran Saeed, M.Fatih Adak

“…A novel method using full homomorphic encryption (FHE) image-based data is proposed, incorporating three different methods to search for the desired encrypted images stored in the cloud. …”
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Assessing the Fidelity and Utility of Water Systems Data Using Generative Adversarial Networks: A Technical Review by Md Nazmul Kabir Sikder, Yingjie Wang, Feras A. Batarseh

“…This review paper addresses this limitation by utilizing Generative Adversarial Networks (GANs) to generate realistic synthetic datasets, overcoming data scarcity and privacy concerns in WDSs. We review, train, and evaluate seven state-of-the-art GAN models using three multivariate time-series datasets. …”
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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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Travel Behavior during the 2021 British Columbia Floods Using De-identified Network Mobility Data by Enqi Liao, Syeda N. Zehra, Stephen D. Wong

“…This study investigates residents’ emergent travel behavior before, during, and after the 2021 British Columbia Floods. Using de-identified network mobility data, we analyze travel patterns centered around the municipality of Hope in British Columbia, Canada. …”
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Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data by Shehu Mohammed, Neha Malhotra

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AN APPROACH PRIORITIZING THE CAUSAL FACTORS OF LARGE SCALED DATA USING SOFT COMPUTING: A CASE STUDY by Jyoti Prakash MISHRA, Zdzislaw POLKOWSKI, Sambit Kumar MISHRA

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Fused federated learning framework for secure and decentralized patient monitoring in healthcare 5.0 using IoMT by Bassam Almogadwy, Abdulrahman Alqarafi

“…Abstract Federated Learning (FL) enables artificial intelligence frameworks to train on private information without compromising privacy, which is especially useful in the medical and healthcare industries where the knowledge or data at hand is never enough. …”
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An efficient secure predictive demand forecasting system using Ethereum virtual machine by Himani Saraswat, Mahesh Manchanda, Sanjay Jasola

“…The system leverages the Ethereum virtual machine to establish a secure, decentralized, and tamper‐resistant platform for demand prediction while ensuring data integrity and privacy. By utilizing the capabilities of smart contracts and decentralized applications within the Ethereum ecosystem, the proposed system offers an efficient and transparent solution for demand forecasting challenges. …”
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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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Scalable architecture for autonomous malware detection and defense in software-defined networks using federated learning approaches by Ripal Ranpara, Shobhit K. Patel, Om Prakash Kumar, Fahad Ahmed Al-Zahrani

“…Our architecture minimizes privacy risks by ensuring that raw data never leaves the device; only model updates are shared for aggregation at the global level. …”
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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’ 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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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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