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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Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU by Pascal Riedel, Kaouther Belkilani, Manfred Reichert, Gerd Heilscher, Reinhold von Schwerin

“…We propose a bottom-up, privacy-preserving prediction method using differential privacy (DP) to enhance data privacy for energy analytics on the customer side. …”
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Data driven healthcare insurance system using machine learning and blockchain technologies by Irum Matloob, Shoab Khan, Bushra Bashir, Rukaiya Rukaiya, Javed Ali Khan, Hessa Alfraihi

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Blockchain Approach for Healthcare Using Fog Topology and Lightweight Consensus by Aya Laouamri, Sarra Cherbal, Yacine Mosbah, Chahrazed Benrebbouh, Kamir Kharoubi

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Privacy Centric Offline Chatbot using Large Language Models by K. Anjali, K. Vipunsai, K. Ruchitha, M. Bhavani, Ch. China Subba Reddy

“…They also might collect and store the data leading to privacy breaches. This research paper focuses on these problems. …”
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Solving the Privacy-Equity Trade-off in Data Sharing By Using Homophily, Diversity, and <italic>t</italic>-Closeness Based Anonymity Algorithm by Abdul Majeed, Seong Oun Hwang

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PPFL-DCS: Privacy-Preserving Federated Learning Using Neural Transformer and Leveraging Dynamic Client Selection to Accommodate Data Diversity by Nakul Mehta, Nitesh Bharot, John G. Breslin, Priyanka Verma

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A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural Network by Jia Jia, Sathiya Sekar Kumarasamy, Kiran Sree Pokkuluri, K. Suresh Kumar, Thella Preethi Priyanka, Feng Wang

“…So, in our model after completing the node authentication and trust detection, privacy preservation of data is performed using Optimal Key-aided Data Sanitization (OPDS). …”
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Survey of split learning data privacy by QIN Yiqun, MA Xiaojing, FU Jiayun, HU Pingyi, XU Peng, JIN Hai

“…In response to these concerns, the Personal Information Protection Law of the People's Republic of China was promulgated to regulate the collection, use, and transmission of private information. Despite this, machine learning requires a large amount of data, necessitating the development of privacy protection technologies that allow for the collection and processing of data under legal and compliant conditions. …”
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Can Synthetic Data Protect Privacy? by Gidan Min, Junhyoung Oh

“…To systematically evaluate the privacy protection performance of synthetic data generation algorithms (Synthpop, CTGAN, RTVAE, TVAE, DataSynthesizer), this study applied various safety metrics. …”
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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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Towards a privacy debt by Xabier Larrucea, Izaskun Santamaría, Manuel Graña Romay

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Revisiting the use and effectiveness of patient-held records in rural Malawi by Amelia Taylor, Paul Kazembe

“…Aim This paper assessed their use and effectiveness within the health data ecosystem, and their potential impact on patient care. …”
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Adaptive personalized privacy-preserving data collection scheme with local differential privacy by Haina Song, Hua Shen, Nan Zhao, Zhangqing He, Wei Xiong, Minghu Wu, Mingwu Zhang

“…Local differential privacy (LDP) is a state-of-the-art privacy notion that enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. …”
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Developing a Model for Protecting the Privacy of Internet Customers in the Field of Health by Zahra Sharifi, Mohammad Ali Keramati, Mehrzad Minooei

“…In this area, there is sensitive and personal information, and privacy can increase customers’ trust in companies and create a stronger relationship between them.Methods: The target sample was chosen using a criterion-oriented purposeful sampling method. …”
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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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Privacy Auditing of Lithium-Ion Battery Ageing Model by Recovering Time-Series Data Using Gradient Inversion Attack in Federated Learning by Kaspars Sudars, Ivars Namatevs, Arturs Nikulins, Kaspars Ozols

“…The exchange of gradients is a widely used method in modelling systems for machine learning (e.g., distributed training, federated learning) in privacy-sensitive domains. …”
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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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Privacy-utility tradeoff method using multi-variable source coding by Yong-hao GU, Jiu-chuan LIN

“…In the age of big data,data providers need to ensure their privacy,while data analysts need to mine the value of data.So,how to find the privacy-utility tradeoff has become a research hotspot.Current works mostly focus on privacy preserving methods,ignoring the data utility.Based on the current research of privacy utility equilibrium methods,a privacy-utility tradeoff method using multi-variable source coding was proposed to solve the problem that different public datasets in the same database have different privacy requirements.Two results are obtained by simulations.The first result is that the greater the association degree between the private information and public information,the increase of the distortion degree of public information will significantly improve the effect of privacy preservation.The second result is that public information with larger variance should be less distorted to ensure more utility.…”
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