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

Get full text

Impact of perceived privacy and security in the TAM model: The perceived trust as the mediated factors by Yan Zhang

Get full text

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. …”
Get full text

THE IMPACT OF DATA SECURITY ON THE INTENTION OF VIETNAMESE CONSUMERS TO USE E-WALLETS by Thi Thu Huong Hoang, Hoang Nam Vu, Thi Khanh Chi Nguyen

“…This article studies the impact of data security on consumer intentions to use e-wallets in the rapidly growing economy of Vietnam. …”
Get full text

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. …”
Get full text

A Qualitative Study of Researchers Perspective on the Use and Risks of Open Government Data by Emigawaty Emigawaty, Dinda Sukmaningrum, Wiji Nurastuti

“…This study aims to review the potential risks of data openness on government data portals from the perspective of researchers as one of the important actors who use data. …”
Get full text

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. …”
Get full text

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.…”
Get full text

Criminalisation of the illegal use of personal data: comparative approaches and the Chinese choice by Zhilong Guo

“…According to policy analysis, in jurisdictions where information technology such as big data and AI is widely available, for example, China, the illegal use of personal data particularly disrupts the community’s sense of security. …”
Get full text

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

Get full text

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. …”
Get full text

Artificial intelligence in neuroimaging: Opportunities and ethical challenges by Neha Brahma, S. Vimal

“…Issues such as algorithmic bias, data privacy, and the interpretability of AI-driven insights must be addressed to ensure that these technologies are used responsibly and equitably. …”
Get full text

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. …”
Get full text

Verticox+: vertically distributed Cox proportional hazards model with improved privacy guarantees by Florian van Daalen, Djura Smits, Lianne Ippel, Andre Dekker, Inigo Bermejo

“…Abstract Federated learning allows us to run machine learning algorithms on decentralized data when data sharing is not permitted due to privacy concerns. …”
Get full text

Improved SpaceTwist privacy protection method based on anchor optimization algorithm by Zhen-peng LIU, Xuan ZHAO, Ya-wei DONG, Bin ZHANG

“…With location-based services worldwide used,private location data appealed easily in query process which caused serious security problems.So the introduction of SpaceTwist incremental nearest neighbor query algorithm,proposes protection of privacy method combined with improved SpaceTwist location optimization algorithm.The anchor point authentication server added to distributed system structure,user generate a k anonymous area according to their privacy preference and actual environment,using optimization algorithm to generate the anchor point.Forwarding users use the incremental nearest neighbor query throught the anchor point and accurate.Experiments in road network environment with different data sets show that the privacy protection works well in the algorithm,and own high work efficiency.…”
Get full text

A robust and personalized privacy-preserving approach for adaptive clustered federated distillation by Mai Shawkat, Zainab H. Ali, Mofreh Salem, Ali El-desoky

“…Meta-learning is used in each cluster to enhance the personalization of the local models and the classification accuracy of the non-independent and Identically distributed (non-IID) data distributions. …”
Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text

Privacy Issues, Attacks, Countermeasures and Open Problems in Federated Learning: A Survey by Blessing Guembe, Sanjay Misra, Ambrose Azeta

“…The results show that privacy and heterogeneity issues are the most common open problems in FL, comprising 38% of the selected articles, while data poisoning emerges as the most common attack, constituting 25% of all attacks identified in the study. …”
Get full text