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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Vehicle Trajectory Generation Based on Generation Adversarial Network by Zhonghe He, Renchi Shao, Sijia Xiang

“…However, the exposure of vehicle location information also raises important privacy issues. Current typical methods for protecting vehicle location privacy protection methods such as anonymity and pseudonymity, still carry the risk of the vehicle being tracked, leading to data security issues. …”
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Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter by Andrew Lowy, Meisam Razaviyayn

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

“…Self-driving vehicles leverage internet of things (IoT) technology to utilize multiple sensors to continuously monitor their environment and make decisions without human intervention. Data collected from these sensors require secure transmission and encrypted storage in cloud servers, where retrieving the data without decryption is necessary to ensure privacy. …”
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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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A comprehensive review of methodologies and application to use the real-world data and analytics platform TriNetX by Ralf J. Ludwig, Ralf J. Ludwig, Ralf J. Ludwig, Matthew Anson, Matthew Anson, Henner Zirpel, Diamant Thaci, Henning Olbrich, Katja Bieber, Khalaf Kridin, Khalaf Kridin, Khalaf Kridin, Astrid Dempfle, Philip Curman, Philip Curman, Philip Curman, Philip Curman, Sizheng S. Zhao, Uazman Alam, Uazman Alam, Uazman Alam

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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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Social media in health care- - Ethically challenging - -Dangerous but attractive- -use with caution by Shrikant B. Mali

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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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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

“…This architecture combines SDN’s centralized management of potentially significant data streams with FL’s decentralized, privacy-preserving learning capabilities in a distributed manner adaptable to varying time and space constraints. …”
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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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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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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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