Verifiable secure aggregation scheme for privacy protection in federated learning networks by Wujun Yao, Tanping Zhou, Yiliang Han, Xiaolin Wang

“…Security analysis demonstrates that our solution effectively ensures privacy protection. We tested the performance using a Raspberry Pi as an edge computing device. …”
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Analysis, Design, and Implementation of a User-Friendly Differential Privacy Application by Reynardo Tjhin, Muhammad Sajjad Akbar, Clement Canonne, Rabia Bashir

“…In the era of artificial intelligence, ensuring privacy in publicly released data is critical to prevent linkage attacks that can reveal sensitive information about individuals. …”
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Digital citizenship literacy in Indonesia: The role of privacy awareness and social campaigns by Rossi Iskandar, Arifin Maksum, Arita Marini

“…A quantitative research approach was employed, using a survey method to collect data from 250 respondents of students from several high schools in Jakarta. …”
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Impact of perceived privacy and security in the TAM model: The perceived trust as the mediated factors by Yan Zhang

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Tabular Data Augmentation Using Artificial Intelligence: A Systematic Review and Taxonomic Framework by Mauro Henrique Lima de Boni, Iwens Gervasio Sene Junior, Ronaldo Martins da Costa

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Blockchain oracles for decentralized agricultural insurance using trusted IoT data by Manoj T, Krishnamoorthi Makkithaya, Narendra V. G., Vijaya Murari T

“…Initially, a method for computing the direct reputation score of IoT devices based on behavioral and data reputation is illustrated. Next, a privacy preserved decentralized oracle mechanism is designed and implemented using a masked secret sharing and secure aggregation scheme. …”
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Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions by R. Vijay Anand, G. Magesh, I. Alagiri, Madala Guru Brahmam, Balamurugan Balusamy, Chithirai Pon Selvan, Haya Mesfer Alshahrani, Masresha Getahun, Ben Othman Soufiene

“…After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. …”
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Data Security Model Using (AES-LEA) Algorithms for WoT Environment by Zinah A. Al-jazaeri, Joolan Rokan Naif, Ahmad Mohamad Ghandour

“…Therefore, ensuring data privacy and protection is a major challenge for organizations and individuals. …”
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Social Engineering Threat Analysis Using Large-Scale Synthetic Data by Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam, Pulasthi Gunawardhana

“…Our model achieved an accuracy of 0.8984 and an F1 score of 0.9253, demonstrating its effectiveness in detecting social engineering attacks. The use of synthetic data overcomes the problem of lack of availability of real-world data due to privacy issues, and is demonstrated in this work to be safe, scalable, ethics friendly and effective.…”
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The secondary use of the Finnish national health data repository Kanta – opportunities and obstacles by Emmi Eronen, Alpo Olavi Värri, Miika Järvinen

“…While protecting the privacy of the individuals’ data in the Kanta Services, improvements to the secondary use law are suggested. …”
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Community views on the secondary use of general practice data: Findings from a mixed‐methods study by Annette J. Braunack‐Mayer, Carolyn Adams, Alberto Nettel‐Aguirre, Belinda Fabrianesi, Lucy Carolan, Justin Beilby, Felicity Flack

“…Abstract Introduction General practice data, particularly when combined with hospital and other health service data through data linkage, are increasingly being used for quality assurance, evaluation, health service planning and research. …”
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Ethical and secure evidence generation from regionwide clinical data through a collaborative environment for advancing predictive care by Dolores Muñoyerro-Muñiz, Román Villegas, Víctor de la Oliva, Alberto Esteban-Medina, Patricia Fernández del Valle, Ana Sánchez, M. Belen Susin, Isidoro Gutierrez-Alvarez, Marta Reboredo, Laura Alejos, Carlos Loucera, Carlos Loucera, Joaquín Dopazo, Joaquín Dopazo

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Clinical Simulation Program for the Training of Health Profession Residents in Confidentiality and the Use of Social Networks by Alejandro Martínez-Arce, Alberto Bermejo-Cantarero, Laura Muñoz de Morales-Romero, Víctor Baladrón-González, Natalia Bejarano-Ramírez, Gema Verdugo-Moreno, María Antonia Montero-Gaspar, Francisco Javier Redondo-Calvo

“…After the course, the participants reported intending to modify their behavior when sharing patient data without their consent and with respect to how patients are informed; Conclusions: The use of advanced simulation in the training of interprofessional teams of residents is as an effective tool for initiating attitudinal change and increasing knowledge related to patient privacy and confidentiality. …”
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Mobile Phone Network Data in the COVID-19 era: A systematic review of applications, socioeconomic factors affecting compliance to non-pharmaceutical interventions, privacy implicat... by Mohammed Okmi, Tan Fong Ang, Muhammad Faiz Mohd Zaki, Chin Soon Ku, Koo Yuen Phan, Irfan Wahyudi, Lip Yee Por

“…<h4>Background</h4>The use of traditional mobility datasets, such as travel surveys and census data, has significantly impacted various disciplines, including transportation, urban sensing, criminology, and healthcare. …”
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Federated learning in food research by Zuzanna Fendor, Bas H.M. van der Velden, Xinxin Wang, Andrea Jr. Carnoli, Osman Mutlu, Ali Hürriyetoğlu

“…The use of machine learning in food research is sometimes limited due to data sharing obstacles such as data ownership and privacy requirements. …”
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Federated meta learning: a review by Chuanyao ZHANG, Shijing SI, Jianzong WANG, Jing XIAO

“…With the popularity of mobile devices, massive amounts of data are constantly produced.The data privacy policies are becoming more and more specified, the flow and use of data are strictly regulated.Federated learning can break data barriers and use client data for modeling.Because users have different habits, there are significant differences between different client data.How to solve the statistical challenge caused by the data imbalance becomes an important topic in federated learning research.Using the fast learning ability of meta learning, it becomes an important way to train different personalized models for different clients to solve the problem of data imbalance in federated learning.The definition and classification of federated learning, as well as the main problems of federated learning were introduced systematically based on the background of federated learning.The main problems included privacy protection, data heterogeneity and limited communication.The research work of federated metalearning in solving the heterogeneous data, the limited communication environment, and improving the robustness against malicious attacks were introduced systematically starting from the background of federated meta learning.Finally, the summary and prospect of federated meta learning were proposed.…”
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ETHICAL DIMENSIONS OF HUMAN RESOURCES AUDITING IN THE DIGITAL ERA by Alice STEFANESCU, Daria MARIN

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Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis by Raja Waseem Anwar, Mohammad Abrar, Abdu Salam, Faizan Ullah

“…Using an FL approach, multiple IoT nodes collaboratively train a global LSTM model without exchanging raw data, thereby addressing privacy concerns and improving detection capabilities. …”
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Blockchain-Enabled Zero Trust Architecture for Privacy-Preserving Cybersecurity in IoT Environments by Mohammed A. Aleisa

“…This architecture entails data privacy and confidentiality, auditability and traceability, and withstanding evolving threats, including potential threats in terms of quantum attacks. …”
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Risk assessment of data protection in the maritime industry using system-theoretic process analysis by Sutthipong Yungratog, Hyungju Kim, Wonsiri Punurai, Sotarat Thammaboosadee

“…Data protection assessment is a significant issue to security and privacy enhancement. …”
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