Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents by Kamela Al-Mannai, Khaled Al-Thelaya, Jens Schneider, Spiridon Bakiras

“…Biometric verification for controlling access to digital devices has become ubiquitous for its ease-of-use but often relies on notoriously data-hungry supervised machine learning. …”
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Investigating the role of serious games on raising students’ awareness on safety and data protection on the Internet by Georgia Zempila, Stelios Xinogalos

“…In this article, we present the results of an empirical study that utilized serious games for raising students’ awareness on safety and data protection on the Internet. Two questionnaires, that collected information regarding students’ knowledge and practices while using the Internet, were used for data collection. …”
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Leveraging data analytics for detection and impact evaluation of fake news and deepfakes in social networks by Tony Mathew Abraham, Tao Wen, Ting Wu, Yu-wang Chen

“…The rampant rise of deepfake technology poses huge risks to social trust and privacy issues, which impacts both individuals and society at large, and leveraging the effective use of data analytics, machine learning and AI techniques can help prevent irreparable damage and mitigate the negative impacts of deepfakes in social networks. …”
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FPGA Implementations for Data Encryption and Decryption via Concurrent and Parallel Computation: A Review by Abdulmajeed Adil Yazdeen, Subhi R. M. Zeebaree, Mohammed Mohammed Sadeeq, Shakir Fattah Kak, Omar M. Ahmed, Rizgar R. Zebari

“… In recent days, increasing numbers of Internet and wireless network users have helped accelerate the need for encryption mechanisms and devices to protect user data sharing across an unsecured network. Data security, integrity, and verification may be used due to these features. …”
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Risk of AI Algorithmic Discrimination Embedded in Government Data Governance and Its Prevention and Control by PENG Lihui, ZHANG Qiong, LI Tianyi

“…To address this issue, this study proposed a series of targeted prevention and control measures, including clarifying the principle of algorithmic fairness, formulating industry norms and standards, improving the accountability mechanism and regulatory system, and optimizing the data collection and processing environment, so as to effectively curb the phenomenon of algorithmic discrimination while making full use of the advantages of AI technology, so that AI technology in government data governance can truly benefit the people, and promote social fairness and justice.…”
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Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects by You-Rak Choi, Yunhee Kang

“…The logger operates a cryptography hash function of a feature vector of a video data frame and an XOR encoding scheme used for hiding the privacy information of sensitive objects in the video data frame. …”
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Verifiable Blockchain-Empowered Federated Learning for Secure Data Sharing in the Internet of Medical Things by Ping Guo, Shuilong Xu, Wenfeng Liang

“…This paper proposes a verifiable blockchain-empowered federated learning framework to address privacy and aggregation verification challenges in data sharing. …”
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Achieving Secure and Efficient Data Access Control for Cloud-Integrated Body Sensor Networks by Zhitao Guan, Tingting Yang, Xiaojiang Du

“…However, how to keep data security and data privacy in cloud-integrated body sensor network (C-BSN) is an important and challenging issue since the patients’ health-related data are quite sensitive. …”
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Implementing Blockchain Technology for Secure Data Transactions in Cloud Computing Environments Challenges and Solutions by Dandu Varalakshmi, N Shirisha, K Suresh, Saidhbi Shaik, V Venkatesh, C F Theresa Cenate

“…We note that while existing security frameworks such as security standards may be useful: they fail to deal with limitations in areas such as privacy, authentication, and secure transactions across multiple cloud platforms. …”
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Analysis of Model Merging Methods for Continual Updating of Foundation Models in Distributed Data Settings by Kenta Kubota, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

“…To address these issues, we propose a novel framework that integrates model merging with federated learning to enable continual foundation model updates without centralizing sensitive data. In this framework, each client fine-tunes a local model, and the server merges these models using multiple merging strategies. …”
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Adaptive integrated weight unsupervised multi-source domain adaptation without source data by Zhirui Wang, Liu Yang, Yahong Han

“…Existing methods assume that all source domain data can be accessed directly. However, such an assumption is unrealistic and causes data privacy concerns, especially when the source domain labels include personal information. …”
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Big Data Meets Social Networks: A Survey of Analytical Strategies and Research Challenges by Shashank Sheshar Singh, Shashank Singh, Kuldeep Singh, Vishal Srivastava, Harish Kumar Shakya

“…In addition, it covers issues of scalability, data quality, privacy, and real-time analysis. SN analytics can be useful for researchers from across domains like business and marketing, healthcare and biomedicine, social sciences and psychology, politics and governance, cybersecurity and fraud detection, recommendation systems, crisis management, and disaster response to extract crucial information from vast datasets and formulate domain-specific strategies.…”
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Health equity innovation in precision medicine: data stewardship and agency to expand representation in clinicogenomics by Patrick J. Silva, Vasiliki Rahimzadeh, Reid Powell, Junaid Husain, Scott Grossman, Adam Hansen, Jennifer Hinkel, Rafael Rosengarten, Marcia G. Ory, Kenneth S. Ramos

“…Digital tools that preserve privacy, document provenance, and enable the management of data as intellectual property have great promise. …”
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Enhancing cybersecurity through autonomous knowledge graph construction by integrating heterogeneous data sources by Hatoon Alharbi, Ali Hur, Hasan Alkahtani, Hafiz Farooq Ahmad

“…In our research, we construct a cybersecurity knowledge graph (CKG) autonomously using heterogeneous data sources. We further enhance the CKG by applying logical rules and employing graph analytic algorithms. …”
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Perlindungan Data Pribadi Konsumen Oleh Penyelenggara Sistem Elektronik Dalam Transaksi Digital by Rista Maharani, Andria Luhur Prakoso

“…In the field of digital economic transactions, the significance of personal data is increasing, driven by the widespread use of big data. …”
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Big Data and AI Algorithms for Sustainable Development Goals: A Topic Modeling Analysis by Prema Nedungadi, Simi Surendran, Kai-Yu Tang, Raghu Raman

“…Using BERTopic modeling, a machine learning technique, this research systematically analyzes literature from 2013 to 2024, providing an overview of AI and big data applications mapped to SDGs which is a first. …”
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Big Data–Driven Health Portraits for Personalized Management in Noncommunicable Diseases: Scoping Review by Haoyang Du, Jianing Yu, Dandan Chen, Jingjie Wu, Erxu Xue, Yufeng Zhou, Xiaohua Pan, Jing Shao, Zhihong Ye

“…In terms of volume, structured data were widely used (64.29%-100% depending on portrait type), while unstructured data usage varied significantly (19.05%-93.33%). …”
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Contribution and legacy: a qualitative study of older people’s attitudes about sharing their routinely collected health data for research purposes in New Zealand by Engelina Groenewald, Jasmine Appleton, Brendan Hallam, Cristian Gonzalez-Prieto, Susan Yates, Daniel Wilson, Gillian Dobbie, Rosie Dobson, Sarah Cullum

“…Data were analysed using thematic analysis. Results Themes identified were: 1) Benefits: participants believed that there were benefits to sharing their health data such as helping others, improving health services, advancing scientific knowledge, and giving back to the health system; sharing health data was also seen as a reflection of good character, and people felt that their pre-existing views about whether they wished to share health data should be respected even if they were no longer able to consent. 2) Concerns: participants had concerns about sharing data with private companies, the use of inaccurate data, and the potential personal and societal consequences of sharing health data. 3) Expectations: participants encouraged collaboration between institutions in New Zealand, but expected data privacy to be maintained, processes to be transparent and cultural values around data to be respected; there was an expectation those sharing health data (patients or institutions) should benefit from any private sector gains. …”
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From intensive care monitors to cloud environments: a structured data pipeline for advanced clinical decision supportResearch in context by Sijm H. Noteboom, Eline Kho, Maria Galanty, Clara I. Sánchez, Frans C.P. ten Bookum, Denise P. Veelo, Alexander P.J. Vlaar, Björn J.P. van der Ster

“…The feasibility of the pipeline will be demonstrated with the available data through research and clinical use cases. Furthermore, privacy, safety, data quality, and environmental impact are carefully considered in the cloud storage transition. …”
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Barriers impeding research data sharing on chronic disease prevention among the older adults in low-and middle-income countries: a systematic review by Neema Florence Vincent Mosha, Patrick Ngulube

“…A PRISMA flow chart was used to map out the number of studies that were identified, included, and excluded.ResultsFive main barriers were consistently highlighted, including a lack of necessary resources (9, 69%), dealing with complex and sensitive research data (2,15%), lack of policies, procedures, guidelines (5,38%), medical big data processing and integration (2,15%), and inadequate ethical considerations, legal compliance, and privacy protection (6,46%). …”
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