Privacy in consumer wearable technologies: a living systematic analysis of data policies across leading manufacturers by Cailbhe Doherty, Maximus Baldwin, Rory Lambe, Marco Altini, Brian Caulfield

“…In this study, we systematically evaluated the privacy policies of 17 leading wearable technology manufacturers using a novel rubric comprising 24 criteria across seven dimensions: transparency, data collection purposes, data minimization, user control and rights, third-party data sharing, data security, and breach notification. …”
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Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity by Shubhi Shukla, Suraksha Rajkumar, Aditi Sinha, Mohamed Esha, Konguvel Elango, Vidhya Sampath

“…This mitigates adversarial attacks and prevents data leakage. The proposed work uses the Breast Cancer Wisconsin Diagnostic dataset to address critical challenges such as data heterogeneity, privacy-accuracy trade-offs, and computational overhead. …”
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PPT-LBS: Privacy-preserving top-k query scheme for outsourced data of location-based services by Yousheng Zhou, Xia Li, Ming Wang, Yuanni Liu

“…However, while cloud server provides convenience and stability, it also leads to data security and user privacy leakage. Aiming at the problems of insufficient privacy protection and inefficient query in the existing LBS data outsourcing schemes, this paper presents a novel privacy-preserving top-k query for outsourcing situations. …”
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Efficient Privacy-Preserving Range Query With Leakage Suppressed for Encrypted Data in Cloud-Based Internet of Things by Sultan Basudan, Abdulrahman Alamer

“…To protect user privacy, the acquired data may be encrypted; however, this often presents challenges for efficiently searching the data. …”
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Privacy-based medical data protection against internal security threats in heterogeneous Internet of Medical Things by Muhammad Asif Habib, C M Nadeem Faisal, Shahzad Sarwar, Muhammad Ahsan Latif, Farhan Aadil, Mudassar Ahmad, Rehan Ashraf, Muazzam Maqsood

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Privacy-Preserving Poisoning-Resistant Blockchain-Based Federated Learning for Data Sharing in the Internet of Medical Things by Xudong Zhu, Hui Li

“…Although current blockchain-based federated learning (BFL) approaches aim to resolve these issues, two persistent security weaknesses remain: privacy leakage and poisoning attacks. This study proposes a privacy-preserving poisoning-resistant blockchain-based federated learning (PPBFL) scheme for secure IoMT data sharing. …”
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Spatio-Temporal Joint Planning for Integrated Energy Systems with Internet Data Center Considering Privacy Preservation by WU Chengbang, CHENG Zhijiang, LU Haifeng, YANG Handi

“…However， because both IDC and integrated energy systems （IES） possess underlying user information， data leakage may lead to various risks. Therefore， when designing collaborative optimization solutions for IDCs and IES， it is essential to consider the privacy preservation of both systems. …”
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Optimizing data privacy and security measures for critical infrastructures via IoT based ADP2S technique by Zhenyu Xu, Jinming Wang, Shujuan Feng, Salwa Othmen, Chahira Lhioui, Aymen Flah, Zdenek Slanina

“…This paper uses a reptile search optimization algorithm to offer attuned data protection with privacy scheme (ADP2S). …”
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Privacy-preserving multi-party data joint analysis scheme based on multiset operations in smart contracts by Xin Liu, Huize Gao, Dan Luo, Lanying Liang, Lu Peng, Yuchen Zhang, Shijie Jia, Gang Xu, Xiubo Chen, Baohua Zhang, Yu Gu

“…However, the demand for multi-party data joint analysis within these contracts faces challenges of privacy leakage and malicious deception. …”
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Enhanced Consumer Healthcare Data Protection Through AI-Driven TinyML and Privacy-Preserving Techniques by S. Aanjankumar, Monoj Kumar Muchahari, Shabana Urooj, Ishmeet Kaur, Rajesh Kumar Dhanaraj, Hanan Abdullah Mengash, S. Poonkuntran, Parag Ravikant Kaveri

“…The proposed method with TinyML looks at patient data, like ECG readings and reports of unusual heartbeats, right on local edge devices that have limited resources, enabled immediate privacy checks while using minimal computing power. …”
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Using UMAP for Partially Synthetic Healthcare Tabular Data Generation and Validation by Carla Lázaro, Cecilio Angulo

“…However, such medical data often comprise sensitive patient information, posing challenges regarding data privacy, and are resource-intensive to acquire for significant research purposes. …”
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Privacy-Preserving Rule Induction Using CKKS by Jihyeon Choi, Jina Choi, Younho Lee, Jung-Sik Hong

“…This study introduces a new approach called homomorphic encryption-based rule induction (HORI) algorithm, designed specifically for scenarios where data confidentiality is critical. This method is constructed using CKKS homomorphic encryption to make it work with encrypted data, thereby enhancing the privacy of both training data and the input for inference. …”
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A survey of individuals’ willingness to share real-world data postmortem with researchers by Rachele M. Hendricks-Sturrup, Christine Y. Lu

Subjects: “…Data use…”
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A Hybrid Brain Tumor Classification Using FL With FedAvg and FedProx for Privacy and Robustness Across Heterogeneous Data Sources by N. Sivakumar, Ahmad Raza Khan, Syed Umar, R. N. Ravikumar, I. Bremnavas, Munindra Lunagaria, Krunal Vaghela, Ghanshyam G. Tejani, Sunil Kumar Sharma

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A Hybrid Framework for Enhancing Privacy in Blockchain-Based Personal Data Sharing using Off-Chain Storage and Zero-Knowledge Proofs by Godwin Mandinyenya, Vusumuzi Malele

“…However, its widespread adoption is constrained by challenges such as limited scalability, privacy concerns, and conflicts with regulatory frameworks like the General Data Protection Regulation (GDPR). …”
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A Symmetric Projection Space and Adversarial Training Framework for Privacy-Preserving Machine Learning with Improved Computational Efficiency by Qianqian Li, Shutian Zhou, Xiangrong Zeng, Jiaqi Shi, Qianye Lin, Chenjia Huang, Yuchen Yue, Yuyao Jiang, Chunli Lv

“…By designing a new projection loss function and combining autoencoders with adversarial training, the proposed method effectively balances privacy protection and model utility. Experimental results show that, for financial time-series data tasks, the model using the projection loss achieves a precision of 0.95, recall of 0.91, and accuracy of 0.93, significantly outperforming the traditional cross-entropy loss. …”
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Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation by Alper Karamanlıoğlu, Berkan Demirel, Onur Tural, Osman Tufan Doğan, Ferda Nur Alpaslan

“…We evaluate the system through two studies: (1) a benchmark of 750+ USMLE-style questions validating the medical reasoning of fine-tuned LLMs; and (2) a real-world case study (<i>n</i> = 132, 75.8% first-pass agreement) using de-identified MIMIC-III data to assess triage accuracy and responsiveness. …”
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A Privacy-Preserving RL-Based Secure Charging Coordinator Using Efficient FL for Smart Grid Home Batteries by Amr A. Elshazly, Islam Elgarhy, Mohamed Mahmoud, Mohamed I. Ibrahem, Maazen Alsabaan

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Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning by Huda Kadhim Tayyeh, Ahmed Sabah Ahmed AL-Jumaili

“…Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major problem. …”
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Evaluating the effectiveness of data governance frameworks in ensuring security and privacy of healthcare data: A quantitative analysis of ISO standards, GDPR, and HIPAA in blockch... by Ameer Ahmed, Asjad Shahzad, Afshan Naseem, Shujaat Ali, Imran Ahmad

“…Although there are existing frameworks to govern healthcare data but they have certain limitations in effectiveness of data governance to ensure security and privacy. …”
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