WiFi-Based Location Tracking: A Still Open Door on Laptops by Mariana Cunha, Ricardo Mendes, Yves-Alexandre de Montjoye, Joao P. Vilela

“…Location privacy is a major concern in the current digital society, due to the sensitive information that can be inferred from location data. …”
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Analyzing the concept of solitude with the spatial syntax technique in the spatial organization of rural homes; case Study: Bushehr province by Farshid Mesbah, Behzad Vasigh, Mostafa Masoudinezhad

“…In the next stage, graph analysis technique was used to extract data from the drawn maps using tools such as connectivity, relationship, step depth, metric depth, and angle of view with the help of Depth Map software for sample analysis. …”
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Factors influencing the perceived usability of line pay: An extended technology acceptance model approach by Yogi Tri Prasetyo, Krisna Chandra Susanto, Ko-Hui Chuang, Ruo-Ting Yin, Jia-Wei Chen, Yu-Xuan Zhang, Omar Paolo Benito, Zachariah John A. Belmonte, Maela Madel L. Cahigas, Reny Nadlifatin, Ma. Janice J. Gumasing

“…Utilizing an extended Technology Acceptance Model (TAM), the research integrated additional variables such as social influence, trust, security, and environmental concerns, offering a comprehensive framework to understand user adoption dynamics. 120 Taiwanese participants were surveyed by employing the Purposive Sampling Method and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). …”
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Based on model randomization and adaptive defense for federated learning schemes by Gaofeng Yue, Xiaowei Han

“…Abstract Federated Learning (FL) is a privacy-enhancing technique that enables multiple participants to collaboratively train machine learning models without sharing their local data. …”
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A data management system for precision medicine. by John J L Jacobs, Inés Beekers, Inge Verkouter, Levi B Richards, Alexandra Vegelien, Lizan D Bloemsma, Vera A M C Bongaerts, Jacqueline Cloos, Frederik Erkens, Patrycja Gradowska, Simon Hort, Michael Hudecek, Manel Juan, Anke H Maitland-van der Zee, Sergio Navarro-Velázquez, Lok Lam Ngai, Qasim A Rafiq, Carmen Sanges, Jesse Tettero, Hendrikus J A van Os, Rimke C Vos, Yolanda de Wit, Steven van Dijk

“…This paper evaluates a MedDMS in five types of use cases for precision medicine, ranging from data collection to algorithm development and from implementation to integration with real-world data. …”
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Medical data sharing and synthetic clinical data generation – maximizing biomedical resource utilization and minimizing participant re-identification risks by Simeone Marino, Ruth Cassidy, Joseph Nanni, Yuxuan Wang, Yipeng Liu, Mingyi Tang, Yuan Yuan, Toby Chen, Anik Sinha, Balaji Pandian, Ivo D. Dinov, Michael L. Burns

“…This article introduces an end-to-end, titratable pipeline that generates privacy-preserving “digital twin” datasets from complex EHR and wearable-device records (Apple Watch data from 3029 participants) using DataSifter and Synthetic Data Vault (SDV) methods. …”
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Cybersecurity and Liability in a Big Data World by Maria Lillà Montagnani, Mirta Antonella Cavallo

“…We conclude by showing that, despite this articulate framework, big data and cloud service providers still leverage on their strong market power to use “contractual shields” and escape liability. …”
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Data Protection in Pre-Tertiary Schools In Ghana by Ephrem Kwaa-Aidoo, Elizabeth Serwaa Baah

“…The study also identifies a lack of coordination, formal guidance, inadequate consent procedures, and fragmented implementation strategies in data protection practices. Furthermore, the use of varied data protection mechanisms by different schools suggests a lack of standardised security protocols, which is likely to result in security gaps. …”
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Heterogeneous bioinformatic data encryption on portable devices by Hao Chen, Xiayun Hong, Yao Cheng, Xiaotong Wang, Liyan Chen, Xuan Cheng, Juncong Lin

“…Abstract With the popularity of mobile health monitoring and genome sequencing techniques, the scale of biomedical and genomic data grow rapidly, with their privacy receiving more and more concerns. …”
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Using electronic health record data to establish a chronic kidney disease surveillance system in China: protocol for the China Kidney Disease Network (CK-NET)-Yinzhou Study by Yu Yang, Peng Shen, Hongbo Lin, Luxia Zhang, Minghui Zhao, Jinwei Wang, Beiyan Bao, Guilan Kong, Xiaoyu Sun, Guohui Ding, Bixia Gao, Chao Yang

“…The China Kidney Disease Network (CK-NET) was established as a comprehensive surveillance system for CKD using various data sources. As part of this, the proposed CK-NET-Yinzhou study aims to build a regional surveillance system in a developed coastal area in China to obtain detailed dynamic information about kidney disease and to improve the ability to manage the disease effectively.Methods and analysis Yinzhou is a district of Ningbo city, Zhejiang province. …”
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Blockchain Technology for Secure Patient Data Sharing by Robert-Stefan CIZMAŞ

“… This paper presents a decentralized healthcare data management system that utilizes blockchain technology to address critical challenges in data security, patient privacy, and interoperability of electronic medical records (EMR). …”
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The Venus score for the assessment of the quality and trustworthiness of biomedical datasets by Davide Chicco, Alessandro Fabris, Giuseppe Jurman

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GraphXplore: Visual exploration and accessible preprocessing of medical data by Louis Bellmann, Karl Gottfried, Philipp Breitfeld, Frank Ückert

“…It enables explainable, easy-to-use exploratory data analysis paired with dataset preparation and metadata annotation accessible for physicians. …”
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MKD-Net: A Novel Neuro Evolutionary Approach for Blockchain-Based Secure Medical Image Classification Using Multi-Kernel DLM by Virendra P. Vishwakarma, Abhay Kumar Yadav

“…Moreover, the integration with blockchain provide secure feature storage and also ensures data integrity, traceability, and enhanced privacy. …”
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Mannaging medical records with blockchain by Paulo Caetano da Silva, Reginalda Santos Silva, Daniel José Diaz

“…Therefore, the goal of this work is to evaluate, through a systematic literature review (SLR), the use of blockchain to guarantee data privacy and security in e-Health systems. …”
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A secure and efficient public auditing system of cloud storage based on BLS signature and automatic blocker protocol by Baidaa Abdulrahman Jalil, Taha Mohammed Hasan, Ghassan Sabeeh Mahmood, Hazim Noman Abed

“…Hence, public cloud storage auditing is critical, so users can use a Third-party auditor (TPA) to verify data integrity. …”
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Patient Confidentiality of Electronic Health Records: A Recent Review of the Saudi Literature by Nada Saddig Almaghrabi, Bussma Ahmed Bugis

“…Background Health systems harbor lucrative data that can be targeted for illegal access, thus posing a serious privacy breach. …”
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Edge Computing and Cloud Computing for Internet of Things: A Review by Francesco Cosimo Andriulo, Marco Fiore, Marina Mongiello, Emanuele Traversa, Vera Zizzo

“…These hybrid models optimize bandwidth use and support low-latency, privacy-sensitive applications in IoT ecosystems. …”
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Attribute-based encryption schema with group signatures by Xinglan ZHANG, Yao CUI

“…Cipher text-policy attribute-based encryption schema(CP-ABE) is widely used in protection of sensitive data.In CP-ABE,When the users access the cipher text,the access structure and the cipher text were sent to the users.However,the access structure is also has private information which can lead to leak of the privacy.The group signatures were used in CP-ABE to realize protection of the privacy.What’s more,it can satisfy indistinguishable under the choice of plaintext attack.…”
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Modelling Users` Trust in Online Social Networks by Iacob Cătoiu, Mihai Orzan, Octav-Ionuţ Macovei, Claudia Iconaru

“…We have adapted metrics for the purpose of our study and we have assessed their reliability and validity. We use a Partial Least Squares (PLS) based structural equation modelling analysis, which validated all our initial assumptions, indicating that our three predictors (privacy concerns, perceived benefits and perceived risks) explain 48% of the variation of users’ trust in online social networks, the resulting variable of our study. …”
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