Data Fusion Algorithm of Privacy Protection Based on Qos and Multilayers Hierarchically by Li Li, Qin Qin, Li Hua, Li Jian

“…Because of the special nature of wireless sensor networks, data fusion process is vulnerable to attacks by the destroyer, which is useful for reducing the network communication overhead and improving the data transmission efficiency. …”
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Investigating privacy risks in open government data: an exploratory case study by Daniel Carter, Caroline Stratton

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The Formal Analysis on Negative Information Selections for Privacy Protection in Data Publishing by Ping Chen, Jingjing Hu, Zhitao Wu, Ruoting Xiong, Wei Ren

“…Negative information selection is an approach to protect the privacy by using negative information to replace original information. …”
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Data Fusion and Processing Technology of Wireless Sensor Network for Privacy Protection by Lusheng Shi, Kai Li, Huibo Zhu

“…Data fusion and privacy protection technologies are both the research focuses in the field of wireless sensor networks. …”
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Promoting sustainable and personalized travel behaviors while preserving data privacy by Cláudia Brito, Noela Pina, Tânia Esteves, Ricardo Vitorino, Inês Cunha, João Paulo

“…With this, three main challenges emerge: (i) increase users’ awareness regarding their carbon footprint, (ii) provide personalized recommendations and incentives for using sustainable transportation alternatives and, (iii) guarantee that any personal data collected from the user is kept private.This paper addresses these challenges by proposing a new methodology. …”
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Review of privacy computing techniques for multi-party data fusion analysis by LIU Shenglong, HUANG Xiuli, JIANG Yiwen, JIANG Jiawei, TIAN Yuechi, ZHOU Zejun, NIU Ben

“…In the data era, threats to personal privacy information in ubiquitous sharing environments are widespread, such as apps frequently collecting personal information beyond scope, and big data-enabled price discrimination against frequent customers. …”
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A systematic review of data privacy in Mobility as a Service (MaaS) by Zineb Garroussi, Antoine Legrain, Sébastien Gambs, Vincent Gautrais, Brunilde Sansò

“…Using the PRISMA framework, a comprehensive literature search across Web of Science, Elsevier, and IEEE Xplore databases resulted in the selection of 32 studies for detailed analysis.The review is structured around three main themes: (1) Privacy-Preserving Techniques, including anonymization strategies (k-anonymity, differential privacy, obfuscation), encryption methods (blockchain, cryptographic protocols), federated learning for decentralized data processing, and advanced algorithms for optimizing privacy budgets and balancing utility-privacy trade-offs; (2) User Trust and Privacy Perceptions, highlighting that trust in service providers is essential for MaaS adoption, privacy concerns may impact adoption but do not necessarily prevent it (the “privacy paradox”), and awareness of data misuse affects user trust and willingness to adopt MaaS; and (3) Regulatory Frameworks, focusing on the importance of GDPR compliance to ensure strict data protection through consent and transparency, and embedding privacy-by-design principles within MaaS architectures to safeguard user data from the outset.This review emphasizes the need for a holistic approach, integrating technological innovation, user-centered design, and strong regulatory oversight to effectively address privacy challenges in MaaS. …”
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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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K-Means Clustering with Local Distance Privacy by Mengmeng Yang, Longxia Huang, Chenghua Tang

“…K-means clustering has been widely used for cluster analysis in real life. However, these analyses are based on users’ data, which disclose users’ privacy. …”
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Privacy-Preserving Modeling of Trajectory Data: Secure Sharing Solutions for Trajectory Data Based on Granular Computing by Yanjun Chen, Ge Zhang, Chengkun Liu, Chunjiang Lu

“…Meanwhile, this work demonstrates the practical applications of the solution for the secure sharing of trajectory data. It integrates trajectory data with economic data using the Takagi–Sugeno fuzzy rule model to fit and predict regional economies, thereby verifying the feasibility of the granular computing model based on differential privacy and ensuring the privacy and security of users’ trajectory information. …”
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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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Smart home: Keeping privacy based on Air‐Padding by Quanlong Zhu, Chao Yang, Yu Zheng, Jianfeng Ma, Hui Li, Junwei Zhang, Jiajie Shao

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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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Context-Contingent Privacy Concerns and Exploration of the Privacy Paradox in the Age of AI, Augmented Reality, Big Data, and the Internet of Things: Systematic Review by Christian Herriger, Omar Merlo, Andreas B Eisingerich, Annisa Rizkia Arigayota

“… BackgroundDespite extensive research into technology users’ privacy concerns, a critical gap remains in understanding why individuals adopt different standards for data protection across contexts. …”
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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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Dynamic Cohort Formation with Hierarchical Blockchain Using GDP for Enhanced FL by Sunila Fatima Ahmad, Zahra Abbas, Madiha Haider Syed, Adeel Anjum, Semeen Rehman

“…In addition, Gaussian Differential Privacy (GDP) is used as a privacy-preserving mechanism that adds controlled noise to the data or model updates to protect individual data points from being inferred by adversaries. …”
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An Efficient Privacy-preserving Deep Learning Scheme for Medical Image Analysis by J. Andrew Onesimu, J Karthikeyan

“…Data providers morph the images without privacy information using image morphing component. …”
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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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