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

“…The paper highlights critical gaps and outlines future research directions for improving smart grid resilience using federated intelligence.…”
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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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The method for approximate continuous Skyline query based on privacy protection in road network by Song Li, Xiaolong Yang, Liping Zhang, Guanglu Sun

“…Abstract Existing road network continuous Skyline queries often neglect user location privacy and data privacy protection. This paper proposes a method for Approximate Continuous Skyline Query with Privacy Protection in road network (ACPP) environments to address these issues. …”
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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

“…This paper proposes a practical and ready-to-use methodology for re-identification risk assessment, the originality of which is manifold: (1) it is the first to follow well-known risk analysis methods (e.g. …”
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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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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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Health data sharing in Germany: individual preconditions, trust and motives by Elias Kühnel, Felix Wilke

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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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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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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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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

“…To support the cost-effectiveness modeling of data security and privacy in Healthcare 4.0 scenarios, the Privacy-Preserving Health 4.0 (PPH 4.0) framework was proposed by integrating Machine Learning (ML) and Elementary Cellular Automata (ECAs). …”
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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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