A scoping review of privacy and utility metrics in medical synthetic data by Bayrem Kaabachi, Jérémie Despraz, Thierry Meurers, Karen Otte, Mehmed Halilovic, Bogdan Kulynych, Fabian Prasser, Jean Louis Raisaro

“…Abstract The use of synthetic data is a promising solution to facilitate the sharing and reuse of health-related data beyond its initial collection while addressing privacy concerns. …”
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Reconsidering big data security and privacy in cloud and mobile cloud systems by Lo'ai A. Tawalbeh, Gokay Saldamli

“…Secondly, we explore the use of P2P Cloud System (P2PCS) for big data processing and analytics. …”
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A hybrid-security model for privacy-enhanced distributed data mining by Tanzeela Javid, Manoj Kumar Gupta, Abhishek Gupta

“…The accuracy obtained in classification task and clustering task using naïve Bayes and k-means technique is high when different datasets in a privacy-enhanced distributed data mining environment verify the working of the hybrid security model.…”
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Privacy-Preserving Substring Search on Multi-Source Encrypted Gene Data by Shiyue Qin, Fucai Zhou, Zongye Zhang, Zifeng Xu

“…Substring searching on gene sequence data is widely used for analyzing the association between a list of gene mutations and a specific disease. …”
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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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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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Perturbation based privacy preservation and classification using Jaya Algorithm and Dragonfly Inspired Algorithm by Dipanwita Sen, Bhupati Bhusan Mishra, Prasant Kumar Pattnaik

“…That is why, privacy preservation of data for healthcare dataset is a significant consideration. …”
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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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Doubly Structured Data Synthesis for Time-Series Energy-Use Data by Jiwoo Kim, Changhoon Lee, Jehoon Jeon, Jungwoong Choi, Joseph H. T. Kim

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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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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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A privacy-enhanced framework for collaborative Big Data analysis in healthcare using adaptive federated learning aggregation by Rahul Haripriya, Nilay Khare, Manish Pandey, Sreemoyee Biswas

“…Abstract The exponential growth of Big Data in healthcare, particularly in AI-driven medical diagnostics, has raised critical concerns about data privacy in medical image classification. …”
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An approach that utilizes blockchain to effectively and securely preserve data privacy for location data from IoT in smart cities by D. Rawal, J. Seedorf, B. Patil

“…Environmental surveillance, emergency response, and smart city planning all require the use of geospatial data, which includes satellite imagery, cartographic records, and real-time GPS coordinates. …”
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