FWFA: Fairness-Weighted Federated Aggregation for Privacy-Aware Decision Intelligence by Rahul Haripriya, Nilay Khare, Manish Pandey, Shrijal Patel, Jaytrilok Choudhary, Dhirendra Pratap Singh, Surendra Solanki, Duansh Sharma

“…As machine learning (ML) and artificial intelligence (AI) increasingly influence such decisions, promoting responsible AI that minimizes bias while preserving data privacy has become essential. However, existing fairness-aware models are often centralized or ill-equipped to handle non-IID data, limiting their real-world applicability. …”
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Preliminary study on the construction of a data privacy protection course based on a teaching-in-practice range by Zhe SUN, Hong NING, Lihua YIN, Binxing FANG

“…Since China’s Data Security Law, Personal Information Protection Law and related laws were formalized, demand for privacy protection technology talents has increased sharply, and data privacy protection courses have been gradually offered in the cyberspace security majors of various universities.Building on longstanding practices in data security research and teaching, the teaching team of “Academician Fang Binxing’s Experimental Class” (referred to as “Fang Class”) at Guangzhou University has proposed a teaching method for data privacy protection based on a teaching-in-practice range.In the selection of teaching course content, the teaching team selected eight typical key privacy protection techniques including anonymity model, differential privacy, searchable encryption, ciphertext computation, adversarial training, multimedia privacy protection, privacy policy conflict resolution, and privacy violation traceability.Besides, the corresponding teaching modules were designed, which were deployed in the teaching practice range for students to learn and train.Three teaching methods were designed, including the knowledge and application oriented teaching method which integrates theory and programming, the engineering practice oriented teaching method based on algorithm extension and adaptation, and the comprehensive practice oriented teaching method for practical application scenarios.Then the closed loop of “learning-doing-using” knowledge learning and application was realized.Through three years of privacy protection teaching practice, the “Fang class” has achieved remarkable results in cultivating students’ knowledge application ability, engineering practice ability and comprehensive innovation ability, which provided useful discussion for the construction of the initial course of data privacy protection.…”
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Blockchain-Enhanced Privacy and Security in Electronic Health Records: A Scalable Framework for Decentralised Data Management by Nagaraj Segar, Vijayarajan Vijayan

“…The framework uniquely integrates a sidechain-enabled blockchain architecture with hybrid encryption (AES-256/RSA and ECC), optimising both scalability and data protection, and is validated using the real-world MIMIC-III dataset. …”
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A Tailored Compliance Solution for Securing Personal Data privacy under Law 18-07 in Algeria by Mohamed kamel Benkaddour, Redouane Guettal, Ph.D

“…The rapid flow of information and the ubiquity of technology pose significant challenges to data security and privacy. In response to these concerns, the Algerian state enacted Law 18-07 on June 10, 2018, aimed at protecting individuals concerning the processing of their data. …”
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Privacy in consumer wearable technologies: a living systematic analysis of data policies across leading manufacturers by Cailbhe Doherty, Maximus Baldwin, Rory Lambe, Marco Altini, Brian Caulfield

“…In this study, we systematically evaluated the privacy policies of 17 leading wearable technology manufacturers using a novel rubric comprising 24 criteria across seven dimensions: transparency, data collection purposes, data minimization, user control and rights, third-party data sharing, data security, and breach notification. …”
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Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity by Shubhi Shukla, Suraksha Rajkumar, Aditi Sinha, Mohamed Esha, Konguvel Elango, Vidhya Sampath

“…This mitigates adversarial attacks and prevents data leakage. The proposed work uses the Breast Cancer Wisconsin Diagnostic dataset to address critical challenges such as data heterogeneity, privacy-accuracy trade-offs, and computational overhead. …”
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PPT-LBS: Privacy-preserving top-k query scheme for outsourced data of location-based services by Yousheng Zhou, Xia Li, Ming Wang, Yuanni Liu

“…However, while cloud server provides convenience and stability, it also leads to data security and user privacy leakage. Aiming at the problems of insufficient privacy protection and inefficient query in the existing LBS data outsourcing schemes, this paper presents a novel privacy-preserving top-k query for outsourcing situations. …”
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Efficient Privacy-Preserving Range Query With Leakage Suppressed for Encrypted Data in Cloud-Based Internet of Things by Sultan Basudan, Abdulrahman Alamer

“…To protect user privacy, the acquired data may be encrypted; however, this often presents challenges for efficiently searching the data. …”
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Privacy-based medical data protection against internal security threats in heterogeneous Internet of Medical Things by Muhammad Asif Habib, C M Nadeem Faisal, Shahzad Sarwar, Muhammad Ahsan Latif, Farhan Aadil, Mudassar Ahmad, Rehan Ashraf, Muazzam Maqsood

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Privacy-Preserving Poisoning-Resistant Blockchain-Based Federated Learning for Data Sharing in the Internet of Medical Things by Xudong Zhu, Hui Li

“…Although current blockchain-based federated learning (BFL) approaches aim to resolve these issues, two persistent security weaknesses remain: privacy leakage and poisoning attacks. This study proposes a privacy-preserving poisoning-resistant blockchain-based federated learning (PPBFL) scheme for secure IoMT data sharing. …”
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Spatio-Temporal Joint Planning for Integrated Energy Systems with Internet Data Center Considering Privacy Preservation by WU Chengbang, CHENG Zhijiang, LU Haifeng, YANG Handi

“…However， because both IDC and integrated energy systems （IES） possess underlying user information， data leakage may lead to various risks. Therefore， when designing collaborative optimization solutions for IDCs and IES， it is essential to consider the privacy preservation of both systems. …”
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Optimizing data privacy and security measures for critical infrastructures via IoT based ADP2S technique by Zhenyu Xu, Jinming Wang, Shujuan Feng, Salwa Othmen, Chahira Lhioui, Aymen Flah, Zdenek Slanina

“…This paper uses a reptile search optimization algorithm to offer attuned data protection with privacy scheme (ADP2S). …”
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Privacy-preserving multi-party data joint analysis scheme based on multiset operations in smart contracts by Xin Liu, Huize Gao, Dan Luo, Lanying Liang, Lu Peng, Yuchen Zhang, Shijie Jia, Gang Xu, Xiubo Chen, Baohua Zhang, Yu Gu

“…However, the demand for multi-party data joint analysis within these contracts faces challenges of privacy leakage and malicious deception. …”
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Enhanced Consumer Healthcare Data Protection Through AI-Driven TinyML and Privacy-Preserving Techniques by S. Aanjankumar, Monoj Kumar Muchahari, Shabana Urooj, Ishmeet Kaur, Rajesh Kumar Dhanaraj, Hanan Abdullah Mengash, S. Poonkuntran, Parag Ravikant Kaveri

“…The proposed method with TinyML looks at patient data, like ECG readings and reports of unusual heartbeats, right on local edge devices that have limited resources, enabled immediate privacy checks while using minimal computing power. …”
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Secondary Use of Electronic Health Record: Opportunities and Challenges by Shahid Munir Shah, Rizwan Ahmed Khan

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Approaches to Federated Computing for the Protection of Patient Privacy and Security Using Medical Applications by Osman Sirajeldeen Ahmed, Emad Eldin Omer, Samar Zuhair Alshawwa, Malik Bader Alazzam, Reefat Arefin Khan

“…The aim of this study is that Medical Applications claims no data is transferred, thereby protecting privacy. …”
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Lightweight privacy preservation blockchain framework for healthcare applications using GM-SSO by Adla Padma, Mangayarkarasi Ramaiah

“…Data security and privacy are crucial for the Internet of Medical Things (IoMT) and the digitization of healthcare systems. …”
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A Privacy‐Preserving System for Confidential Carpooling Services Using Homomorphic Encryption by David Palma, Pier Luca Montessoro, Mirko Loghi, Daniele Casagrande

“…Experimental results indicate that the proposed solution offers robust resistance to various attacks, including replay attacks and data exposure, providing a robust and privacy‐centric solution for carpooling services.…”
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Fraud Detection in Privacy Preserving Health Insurance System Using Blockchain Technology by Md. Mazharul Islam, Mubasshir Ahmed, Rajesh Palit, Mohammad Shahriar Rahman, Salekul Islam

“…To address these issues, this paper proposes a system that ensures patient anonymity through secure credentials and advanced fraud detection mechanisms. Privacy is preserved using cryptographic techniques such as secure hashing and anonymous credentials, which ensure that sensitive patient information remains confidential throughout the claim process. …”
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A Symmetric Projection Space and Adversarial Training Framework for Privacy-Preserving Machine Learning with Improved Computational Efficiency by Qianqian Li, Shutian Zhou, Xiangrong Zeng, Jiaqi Shi, Qianye Lin, Chenjia Huang, Yuchen Yue, Yuyao Jiang, Chunli Lv

“…By designing a new projection loss function and combining autoencoders with adversarial training, the proposed method effectively balances privacy protection and model utility. Experimental results show that, for financial time-series data tasks, the model using the projection loss achieves a precision of 0.95, recall of 0.91, and accuracy of 0.93, significantly outperforming the traditional cross-entropy loss. …”
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