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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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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Cyber-Biosecurity Challenges in Next-Generation Sequencing: A Comprehensive Analysis of Emerging Threat Vectors by Nasreen Anjum, Hani Alshahrani, Asadullah Shaikh, Mahreen-Ul-Hassan, Mehreen Kiran, Shah Raz, Abu Alam

“…Genomic data is inherently sensitive, and vulnerabilities in NGS technologies, software, data-sharing practices, and open-access databases expose it to risks concerning data confidentiality, integrity, and privacy. …”
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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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Navigating the Digital Twin Network landscape: A survey on architecture, applications, privacy and security by Akshita Maradapu Vera Venkata Sai, Chenyu Wang, Zhipeng Cai, Yingshu Li

“…The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. …”
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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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Implantable Medical Device Security by Luigi Catuogno, Clemente Galdi

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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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Federated Learning-Based Load Forecasting for Energy Aggregation Service Providers by HUANG Yichuan, SONG Yuhui, JING Zhaoxia

“…However，the required forecasting data are typically scattered among different energy suppliers，and owing to data security and privacy concerns，these suppliers are often unwilling to share their data. …”
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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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Detecting Personally Identifiable Information Through Natural Language Processing: A Step Forward by Luca Mainetti, Andrea Elia

“…The protection of personally identifiable information (PII) is being increasingly demanded by customers and governments via data protection regulations. Private and public organizations store and exchange through the Internet a large amount of data that include the personal information of users, employees, and customers. …”
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LLM-MalDetect: A Large Language Model-Based Method for Android Malware Detection by Ruirui Feng, Hui Chen, Shuo Wang, Md Monjurul Karim, Qingshan Jiang

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A Systematic Literature Review on Privacy Preservation in VANETs: Trends, Challenges, and Future Directions by Esti Rahmawati Agustina, Kalamullah Ramli, Arif Rahman Hakim, Ruki Harwahyu

“…Privacy preservation is a fundamental requirement in vehicular ad hoc networks (VANETs) because it addresses sensitive vehicle and driver data. …”
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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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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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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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