Design of an efficient dynamic context‐based privacy policy deployment model via dual bioinspired Q learning optimisations by Namrata Jiten Patel, Ashish Jadhav

“…The combination of ALO, FFO, and Q‐learning techniques offers a practical solution to evolving data privacy challenges and enhances flexibility in various use case scenarios.…”
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Federated intelligence for smart grids: a comprehensive review of security and privacy strategies by Raseel Z. Alshamasi, Dina M. Ibrahim

“…This review paper critically surveys recent advancements in federated learning (FL) as a privacy-preserving machine learning technique for addressing these challenges. …”
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Enhancing Privacy While Preserving Context in Text Transformations by Large Language Models by Tymon Lesław Żarski, Artur Janicki

“…Despite the convenience, many users are unaware of the risks posed to their sensitive and personal data. This study addresses this issue by presenting a comprehensive solution to prevent personal data leakage using online tools. …”
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Balancing Ethics and Privacy in the Use of Artificial Intelligence in Institutions of Higher Learning: A Framework for Responsive AI Systems by Belinda Ndlovu, Kudakwashe Maguraushe

“…Thematic analysis identified ten critical themes centred around benefits, challenges, applications, responsible use, privacy and data security, ethical considerations, institutional policies and frameworks, training, equity, and sustainable AI use. …”
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Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption by William J. Buchanan, Hisham Ali

“…The requirement for privacy-aware machine learning increases as we continue to use PII (personally identifiable information) within machine training. …”
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Enhancing Data Security in Distributed Systems Using Homomorphic Encryption and Secure Computation Techniques by Parihar Bhawana, Kiran Ajmeera, Valaboju Sabitha, Rashid Syed Zahidur, Liyakat Kazi Kutubuddin Sayyad, D R Anita Sofia Liz

“…Traditional solutions tend to use standard techniques like basic data wrapping and cryptographic 'rings'; but, due to the design properties required, they end up as lightweight mechanisms, usually not interpretation-at-all capable because of the need for protecting data during processing - leaving these applications hard to use and maintain long-term, or otherwise, limited to cloud computing and federated learning, when individual data types can be worked on within providers like AWS, Azure, etc, etc; or, even, explaining the results with near total indifference to the underlying big data tools, analytics, or neural architectures. …”
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My privacy at risk – my guard is on: a study of SNS use among young adults by Meenakshi Handa, Ronika Bhalla, Parul Ahuja

“…Purpose – Increasing incidents of privacy invasion on social networking sites (SNS) are intensifying the concerns among stakeholders about the misuse of personal data. …”
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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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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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Practical and ready-to-use methodology to assess the re-identification risk in anonymized datasets by Louis Philippe Sondeck, Maryline Laurent

“…Abstract To prove that a dataset is sufficiently anonymized, many privacy policies suggest that a re-identification risk assessment be performed, but do not provide a precise methodology for doing so, leaving the industry alone with the problem. …”
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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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Health data sharing in Germany: individual preconditions, trust and motives by Elias Kühnel, Felix Wilke

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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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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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