Prompts, privacy, and personalized learning: integrating AI into nursing education—a qualitative study by Mingyan Shen, Yanping Shen, Fangchi Liu, Jiawen Jin

“…However, questions remain regarding the ethical use of AI-generated outputs, data privacy concerns, and limitations in recognizing emotional nuances. …”
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A privacy budget adaptive optimization scheme for federated computing power Internet of things by MA Wenyu, CHEN Qian, HU Yuxiang, YAN Haonan, HU Tao, YI Peng

“…However, two severe challenges have not been taken into account in the existing DP budget settings, i.e., data heterogeneity issue of local computing power nodes and the fairness of privacy budget allocation, which lead to a significant loss in model accuracy. …”
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FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification by Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal, Bhavani Sankar Panda, Hardik Doshi, Nafeesh Ahmad, Vivek Bhardwaj, Gaurav Sharma

“…Then implement the federated learning (FL) framework with IID (Independent and identically distributed) and Non-IID datasets maintain similar results by preserving data. Further FL-QNNs approach is used with these baseline models and achieved 98-99% accuracy and while optimizing memory, preserving data and resources. …”
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Touch of Privacy: A Homomorphic Encryption-Powered Deep Learning Framework for Fingerprint Authentication by U. Sumalatha, K. Krishna Prakasha, Srikanth Prabhu, Vinod C. Nayak

“…Deep learning and fully homomorphic encryption (FHE) are integrated for privacy-preserving fingerprint recognition. Convolutional neural network (CNN) extract fingerprint features encrypted using the Cheon-Kim-Kim-Song (CKKS) FHE scheme. …”
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Quantum Privacy Comparison with <i>R_y</i> Rotation Operation by Min Hou, Yue Wu

“…This paper presents a novel quantum privacy comparison (QPC) protocol that employs <i>R_y</i> rotation operations to enable two participants to securely compare their binary secrets without disclosing the actual data to any party except for the comparison result. …”
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PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation by Zhengqiang Ge, Xinyu Liu, Qiang Li, Yu Li, Dong Guo

“…To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. …”
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How Differential Privacy Will Affect Estimates of Air Pollution Exposure and Disparities in the United States by Madalsa Singh

“…Wwith the advent of sophisticated personal datasets and analysis, Census Bureau is considering adding top-down noise (differential privacy) and post-processing 2020 census data to reduce the risk of identification of individual respondents. …”
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Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosis by Wanjun Hao, Shuqin Liu, Chunyang Lv, Yunling Wang, Jianfeng Wang

“…By integrating our protocols with privacy matrix techniques, the cloud server can generate a comprehensive diagnostic model from multiple data sources, offering accurate diagnosis services without disclosing any users’ personal information. …”
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Cancelable Multi-Branch Deep Learning Framework for Privacy-Preserving ECG Biometric Authentication by Mohamed Hammad, Ali Abdullah S. AlQahtani, Mohammed ELAffendi, Abdelhamied A. Ateya, Nebojsa Bacanin

“…However, storing and processing raw ECG data raises privacy and security concerns, requiring advanced methods to safeguard user data. …”
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A Survey on Privacy and Security in Distributed Cloud Computing: Exploring Federated Learning and Beyond by Ahmad Rahdari, Elham Keshavarz, Ehsan Nowroozi, Rahim Taheri, Mehrdad Hajizadeh, Mohammadreza Mohammadi, Sima Sinaei, Thomas Bauschert

“…Particular attention is given to progress in federated learning, which trains a model across multiple devices without sharing the actual data, keeping data private and secure. The survey also highlights federated learning techniques, including secure federated learning, by detecting malicious updates and privacy-preserving federated learning via data encryption, data perturbation, and anonymization, as new paradigms for building responsible computing systems. …”
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A Systematic-Narrative Review of Online Proctoring Systems and a Case for Open Standards by Eva Heinrich

“…However, concerns about data privacy and security, transparency in data processing and decision-making, and the ethical use of online proctoring systems have been raised. …”
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SynAdult: Multimodal Synthetic Adult Dataset Generation via Diffusion Models and Neuromorphic Event Simulation for Critical Biometric Applications by Muhammad Ali Farooq, Paul Kielty, Wang Yao, Peter Corcoran

“…We propose SynAdult, a multimodal synthetic data generation framework designed to address the scarcity of diverse and privacy-compliant senior adult face datasets for biometric applications and facial analysis. …”
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Failure of affine‐based reconstruction attack in regenerating vascular feature points by Mahshid Sadeghpour, Arathi Arakala, Stephen A. Davis, Kathy J. Horadam

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Legal Barriers in Developing Educational Technology by Cam Ai Tran, Truc Thanh Lam Nguyen

“…Through a comparative legal analysis of domestic and international laws, the study sheds light on the legal frameworks affecting technology integration in education. Data privacy issues arise from the sensitive information collected in educational settings, while intellectual property challenges relate to protecting and fairly using digital content and software. …”
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LSTM-JSO framework for privacy preserving adaptive intrusion detection in federated IoT networks by Shaymaa E. Sorour, Mohammed Aljaafari, Amany M. Shaker, Ahmed E. Amin

“…Federated learning is leveraged to enable collaborative model training across multiple IoT networks while maintaining data privacy and reducing vulnerability to data poisoning. …”
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Artificial Intelligence–Enabled Facial Privacy Protection for Ocular Diagnosis: Development and Validation Study by Haizhu Tan, Hongyu Chen, Zhenmao Wang, Mingguang He, Chiyu Wei, Lei Sun, Xueqin Wang, Danli Shi, Chengcheng Huang, Anping Guo

“… Abstract BackgroundFacial biometric data, while valuable for clinical applications, poses substantial privacy and security risks. …”
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Privacy Policy Pop-up: A Genre Analysis of Journal Websites’ HTTP Cookies by Lisbeth Sinan Lendik

“… Online users are made to believe that they have control over their data privacy through the selection of website cookie options. …”
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Enhancing Privacy by Large Mask Inpainting and Fusion-Based Segmentation in Street View Imagery by Mahdi Khourishandiz, Abdollah Amirkhani

“…Despite its reliance on accurate sensor calibration and multimodal data availability, the proposed framework offers a scalable solution for privacy-sensitive applications such as urban mapping, and virtual tourism, delivering high-quality anonymized imagery with minimal artifacts.…”
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BFG: privacy protection framework for internet of medical things based on blockchain and federated learning by Wenkang Liu, Yuxuan He, Xiaoliang Wang, Ziming Duan, Wei Liang, Yuzhen Liu

“…The deep integration of Internet of Medical Things (IoMT) and Artificial intelligence makes the further development of intelligent medical services possible, but privacy leakage and data security problems hinder its wide application. …”
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Federated Learning-Based Trust Evaluation With Fuzzy Logic for Privacy and Robustness in Fog Computing by Thinh Le Vinh, Huan Thien Tran, Huyen Trang Phan, Samia Le

“…This paper introduces a Federated Learning Trust Model (FLTM) to assess trustworthiness across 2000 resources while preserving data privacy. FLTM incorporates six critical metrics: Availability, Reliability, Data Integrity, Identity, Computational Capability, and Throughput. …”
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