Generating unseen diseases patient data using ontology enhanced generative adversarial networks by Chang Sun, Michel Dumontier

“…Abstract Generating realistic synthetic health data (e.g., electronic health records), holds promise for fundamental research, AI model development, and enhancing data privacy safeguards. …”
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Predicting financial default risks: A machine learning approach using smartphone data by Shinta Palupi, Gunawan, Ririn Kusdyawati, Richki Hardi, Rana Zabrina

“…This study leverages machine learning (ML) techniques to predict financial default risks using smartphone data, providing a novel approach to financial risk assessment. …”
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Sample selection using multi-task autoencoders in federated learning with non-IID data by Emre Ardıç, Yakup Genç

“…Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. However, its performance is often hindered by redundant, malicious, or abnormal samples, leading to model degradation and inefficiency. …”
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Technical Innovations and Social Implications: Mapping Global Research Focus in AI, Blockchain, Cybersecurity, and Privacy by Emanuela Bran, Răzvan Rughiniș, Dinu Țurcanu, Gheorghe Nadoleanu

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Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning by Milad Rahmati

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Exploring the Use of AI in Qualitative Data Analysis: Comparing Manual Processing with Avidnote for Theme Generation by S. M. Akramul Kabir, Fareeha Ali, Rana Lotfy Ahmed, Ruqayya Sulaiman-Hill

“…However, fundamental concerns persist regarding the robustness, generalisability, credibility, reliability and trustworthiness of qualitative research when using AI technologies. Ethical considerations, such as data security and privacy, also need to be addressed in research settings. …”
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Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations by Hisham AbouGrad, Lakshmi Sankuru

“…This research study explores a decentralized anomaly detection framework using deep autoencoders, designed to meet the dual imperatives of fraud detection effectiveness and user data privacy. …”
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Social smart city research: interconnections between participatory governance, data privacy, artificial intelligence and ethical sustainable development by Samad Rasoulzadeh Aghdam, Samad Rasoulzadeh Aghdam, Behnaz Bababeimorad, Behnam Ghasemzadeh, Behnam Ghasemzadeh, Behnam Ghasemzadeh, Behnam Ghasemzadeh, Mazdak Irani, Aapo Huovila

“…Four interconnected thematic clusters cropped up: (1) participatory governance, (2) data privacy and security, (3) artificial intelligence and social media, and (4) ethics and sustainable development. …”
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Balancing Data Privacy and 5G VNFs Security Monitoring: Federated Learning with CNN + BiLSTM + LSTM Model by Abdoul-Aziz Maiga, Edwin Ataro, Stanley Githinji

“…The authorities also require data privacy enhancement in 5G deployment and there is the fact that mobile operators need to inspect data for malicious traffic detection. …”
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Federated Learning for Privacy-Preserving Severity Classification in Healthcare: A Secure Edge-Aggregated Approach by Ankita Maurya, Rahul Haripriya, Manish Pandey, Jaytrilok Choudhary, Dhirendra Pratap Singh, Surendra Solanki, Duansh Sharma

“…Federated learning (FL) has emerged as a promising paradigm for privacy-preserving machine learning across decentralized healthcare systems. …”
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Privacy-preserving record linkage by a federated trusted third party (fTTP) – unlocking medical research potential in Germany by Hampf, Christopher, Bialke, Martin, Hund, Hauke, Fegeler, Christian, Lang, Stefan, Penndorf, Peter, Wöller, Nico, Moser, Frank-Michael, Blumentritt, Arne, Schuldt, Ronny, Seidel, Florian, Brunecker, Peter, Wettstein, Reto, Arnecke, Lukas, Hoffmann, Wolfgang

“…In order to comply with the relevant data protection requirements, a privacy-preserving record linkage (PPRL) is required to enable cross-site merging of patient records. …”
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Towards practical intrusion detection system over encrypted traffic* by Sébastien Canard, Chaoyun Li

“…Abstract Privacy and data confidentiality are today at the heart of many discussions. …”
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On signal encryption at MapReduce and collaborative attribute-based access with ECAs for a preprocessed data set with ML in a privacy-preserving health 4.0 by Arnab Mitra, Anabik Pal

“…Latest Industry 4.0 developments and data science advances have transformed traditional hospital-centric patient care into a Healthcare 4.0 system that uses advanced technology-driven decision-making involving several low resource constraints electronic devices such as Personal Digital Assistants (PDAs), Smartphones, Tablets, etc. …”
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Homomorphic signcryption with public plaintext‐result checkability by Shimin Li, Bei Liang, Aikaterini Mitrokotsa, Rui Xue

“…Two notions of message privacy are also investigated: weak message privacy and message privacy depending on whether the original signcryptions used in the evaluation are disclosed or not. …”
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Health data sharing in Germany: individual preconditions, trust and motives by Elias Kühnel, Felix Wilke

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Edge Computing Architectures for Low-Latency Data Processing in Internet of Things Applications by Banoth Sreenu, M Vineesha, Punna Hari Shankar, P Mathiyalagan, Prakash Vijay, M Jasmin

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Risk-adaptive access control model for big data in healthcare by Zhen HUI, Hao LI, Min ZHANG, Deng-guo FENG

“…While dealing with the big data in healthcare,it was difficult for a policy maker to foresee what information a doctor may need,even to make an accurate access control policy.To deal with it,a risk-based access control model that regulates doctors’ access rights adaptively was proposed to protect patient privacy.This model analyzed the history of access,applies the EM algorithm and the information entropy technique to quantify the risk of privacy violation.Using the quantified risk,the model can detect and control the over-accessing and exceptional accessing of patients’ data.Experimental results show that this model is effective and more accurate than other models.…”
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Risk-adaptive access control model for big data in healthcare by Zhen HUI, Hao LI, Min ZHANG, Deng-guo FENG

“…While dealing with the big data in healthcare,it was difficult for a policy maker to foresee what information a doctor may need,even to make an accurate access control policy.To deal with it,a risk-based access control model that regulates doctors’ access rights adaptively was proposed to protect patient privacy.This model analyzed the history of access,applies the EM algorithm and the information entropy technique to quantify the risk of privacy violation.Using the quantified risk,the model can detect and control the over-accessing and exceptional accessing of patients’ data.Experimental results show that this model is effective and more accurate than other models.…”
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Algorithm of blockchain data provenance based on ABE by Youliang TIAN, Kedi YANG, Zuan WANG, Tao FENG

“…To solve the problem that the blockchain-based traceability algorithm mainly used homomorphic encryption and zero-knowledge proof for privacy protection,making it difficult to achieve dynamic sharing of traceability information,a blockchain data traceability algorithm based on attribute encryption was proposed.In order to realize the dynamic protection of transaction privacy,the strategy update algorithm applicable to block chain was designed based on the CP-ABE scheme proposed by Waters to achieve dynamic protection of transaction privacy.In order to realize the dynamic update of the visibility about block content,based on the strategy update algorithm,the block structure was designed to achieve the dynamic update about the content visibility of the block.The security and experimental simulation analysis show that the proposed algorithm can realize the dynamic sharing of traceability information while completing the protection transaction privacy.…”
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Decentralized Voltage Prediction in Multi-Area Distribution Systems: A Privacy-Preserving Collaborative Framework by Jianfeng Yan, Beibei Wang, Zhiqiang Wu, Zhengkai Ding

“…However, potential privacy concerns pose significant challenges to data sharing and interarea collaboration. …”
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