Privacy-preserving federated learning framework with dynamic weight aggregation by Zuobin YING, Yichen FANG, Yiwen ZHANG

“…There are two problems with the privacy-preserving federal learning framework under an unreliable central server.① A fixed weight, typically the size of each participant’s dataset, is used when aggregating distributed learning models on the central server.However, different participants have non-independent and homogeneously distributed data, then setting fixed aggregation weights would prevent the global model from achieving optimal utility.② Existing frameworks are built on the assumption that the central server is honest, and do not consider the problem of data privacy leakage of participants due to the untrustworthiness of the central server.To address the above issues, based on the popular DP-FedAvg algorithm, a privacy-preserving federated learning DP-DFL algorithm for dynamic weight aggregation under a non-trusted central server was proposed which set a dynamic model aggregation weight.The proposed algorithm learned the model aggregation weight in federated learning directly from the data of different participants, and thus it is applicable to non-independent homogeneously distributed data environment.In addition, the privacy of model parameters was protected using noise in the local model privacy protection phase, which satisfied the untrustworthy central server setting and thus reduced the risk of privacy leakage in the upload of model parameters from local participants.Experiments on dataset CIFAR-10 demonstrate that the DP-DFL algorithm not only provides local privacy guarantees, but also achieves higher accuracy rates with an average accuracy improvement of 2.09% compared to the DP-FedAvg algorithm models.…”
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Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning by Saud Sohail, Syed Muhammad Sajjad, Adeel Zafar, Zafar Iqbal, Zia Muhammad, Muhammad Kazim

“…This research focuses on the detection of deepfake images and videos for forensic analysis using deep learning techniques. It highlights the importance of preserving privacy and authenticity in digital media. …”
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Practical and privacy-preserving geo-social-based POI recommendation by Qi Xu, Hui Zhu, Yandong Zheng, Fengwei Wang, Le Gao

“…To protect digital assets, service providers encrypt data before outsourcing it. However, encryption reduces data availability, making it more challenging to provide POI recommendation services in outsourcing scenarios. …”
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Automated Redaction of Personally Identifiable Information on Drug Labels Using Optical Character Recognition and Large Language Models for Compliance with Thailand’s Personal Data... by Parinya Thetbanthad, Benjaporn Sathanarugsawait, Prasong Praneetpolgrang

“…The rapid proliferation of artificial intelligence (AI) across various industries presents both opportunities and challenges, particularly concerning personal data privacy. With the enforcement of regulations like Thailand’s Personal Data Protection Act (PDPA), organizations face increasing pressure to protect sensitive information found in diverse data sources, including product and shipping labels. …”
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Sinergi Pemanfaatan Big Data dengan Sharing Data Privacy Pengguna: Efek Menawarkan Kompensasi dalam Meyakinkan Pengguna E-Wallet by Christina Sudyasjayanti, Laili Rahmania

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Developing a Model for Protecting the Privacy of Internet Customers in the Field of Health by Zahra Sharifi, Mohammad Ali Keramati, Mehrzad Minooei

“…In this area, there is sensitive and personal information, and privacy can increase customers’ trust in companies and create a stronger relationship between them.Methods: The target sample was chosen using a criterion-oriented purposeful sampling method. …”
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Using the LTO Network Level 1 Blockchain to Automate Inter-Organizational Business Processes by Khrypko Serhii L., Shcherbakov Serhii S.

“…The author explains the operation of a private event chain as an ad-hoc private blockchain that ensures the consistency of the process state between nodes. Methods of ensuring data privacy are discussed. The second part of the article is devoted to the global public blockchain LTO to confirm information from private event chains. …”
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Security Data Aggregation with Recoverable Data in Heterogeneous Wireless Sensor Network by Lusheng Shi, Huibo Zhu, Lin Chen

“…The algorithm uses homomorphism encryption techniques based on elliptic curve to address data privacy protection, and uses an efficient aggregate signature scheme to ensure data integrity and authenticity. …”
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(<italic>r, k, &#x03B5;</italic>)-Anonymization: Privacy-Preserving Data Publishing Algorithm Based on Multi-Dimensional Outlier Detection, <italic>k</italic>-Anonymity, and <itali... by Burak Cem Kara, Can Eyupoglu, Oktay Karakus

“…The general data protection regulation (GDPR) implementation, on the other hand, has introduced extensive control over the use of individuals&#x2019; personal information and placed many limits. …”
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Navigating the EU data governance labyrinth: A business perspective on data sharing in the financial sector by Eugénie Coche, Ans Kolk, Martijn Dekker

“…With policy-making (“on the books”) centred on guaranteeing data privacy and data security whilst promoting innovation, firms face complexities when implementing this framework “on the ground”. …”
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Secondary Use of Electronic Health Record: Opportunities and Challenges by Shahid Munir Shah, Rizwan Ahmed Khan

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On the research for big data uses for public good purposes by Adeline Decuyper

“…Finally, aside from the opportunities, the mass production and use of data has also brought many challenges, such as evaluating the representativity of the data or handling threats to the privacy of users, that we will discuss in the last section of this article.…”
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Continuous location privacy protection mechanism based on differential privacy by Hongtao LI, Xiaoyu REN, Jie WANG, Jianfeng MA

“…Aiming at the problem of users’ location privacy leakage caused by continuously using LBS, a road privacy level (RPL) algorithm was proposed based on road topological network, which divided the privacy level of the road sections around the sensitive locations.Then, a differential privacy location protection mechanism (DPLPM) was proposed.Privacy budget was allocated for sensitive road sections and Laplace noise was added to realize the privacy protection of location data.The experimental results show that the mechanism has high data availability while protecting the privacy of location information.…”
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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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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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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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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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Membership Inference Attacks Fueled by Few-Shot Learning to Detect Privacy Leakage and Address Data Integrity by Daniel Jiménez-López, Nuria Rodríguez-Barroso, M. Victoria Luzón, Javier Del Ser, Francisco Herrera

“…Deep learning models have an intrinsic privacy issue as they memorize parts of their training data, creating a privacy leakage. …”
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Federated Analysis With Differential Privacy in Oncology Research: Longitudinal Observational Study Across Hospital Data Warehouses by Théo Ryffel, Perrine Créquit, Maëlle Baillet, Jason Paumier, Yasmine Marfoq, Olivier Girardot, Thierry Chanet, Ronan Sy, Louise Bayssat, Julien Mazières, Vincent Vuiblet, Julien Ancel, Maxime Dewolf, François Margraff, Camille Bachot, Jacek Chmiel

“…Despite some pioneering work, federated analytics is still not widely used on real-world data, and to our knowledge, no real-world study has yet combined it with other privacy-enhancing techniques such as differential privacy (DP). …”
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Navigating Data Privacy in Digital Public Services: Public Perceptions and Policy Implications. Romania Case Study by Mircea POPA

“…However, this reliance on data has raised critical concerns about privacy, security, and ethical data use. …”
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