Privacy Auditing of Lithium-Ion Battery Ageing Model by Recovering Time-Series Data Using Gradient Inversion Attack in Federated Learning by Kaspars Sudars, Ivars Namatevs, Arturs Nikulins, Kaspars Ozols

“…The exchange of gradients is a widely used method in modelling systems for machine learning (e.g., distributed training, federated learning) in privacy-sensitive domains. …”
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Security control scheme for cloud data copy based on differential privacy model by Jun REN, Jin-bo XIONG, Zhi-qiang YAO

“…In cloud computing environment,adversary may use data deduplication as a side channel to eavesdrop users’ privacy.In order to tackle this serious issue,a secure copy number control scheme was proposed.The highlights of the proposed scheme lie in constructing a hybrid cloud framework.Then file was encrypted for second times by using the privacy protection parameters of differential privacy as well as the key of the file computed by convergent encryption algorithm.After the file is uploaded,each file has only one copy in the server.Meanwhile,compared with the scheme that use setting threshold value to control the number of copies,the proposed scheme can resist against the side channel attack,related files attack,and the specific attack of encryption algorithm and save the network bandwidth and disk storage space.…”
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An approach that utilizes blockchain to effectively and securely preserve data privacy for location data from IoT in smart cities by D. Rawal, J. Seedorf, B. Patil

“…Environmental surveillance, emergency response, and smart city planning all require the use of geospatial data, which includes satellite imagery, cartographic records, and real-time GPS coordinates. …”
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Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption by Maulyanda Maulyanda, Rini Deviani, Afdhaluzzikri Afdhaluzzikri

“…Protecting the privacy of medical data while enabling sophisticated data analysis is a critical challenge in modern healthcare. …”
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Privacy, personal data protection and the digital age: A (criminal) law "omnibus" by Banović Jovana M., Radisavljević Ivana M.

“…This complexity is further amplified by the development of artificial intelligence, which inherently relies on the use of vast amounts of data. The aim of this paper is to identify some of the critical elements in the protection of privacy rights, particularly those related to personal data most closely linked to individuals, and to raise the question of potential legislative amendments.…”
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Multi-Smart Meter Data Encryption Scheme Basedon Distributed Differential Privacy by Renwu Yan, Yang Zheng, Ning Yu, Cen Liang

“…Under the general trend of the rapid development of smart grids, data security and privacy are facing serious challenges; protecting the privacy data of single users under the premise of obtaining user-aggregated data has attracted widespread attention. …”
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Publishing neural networks in drug discovery might compromise training data privacy by Fabian P. Krüger, Johan Östman, Lewis Mervin, Igor V. Tetko, Ola Engkvist

“…Abstract This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membership inference attacks, a common method to assess privacy that is largely unexplored in the context of drug discovery, to examine neural networks for molecular property prediction in a black-box setting. …”
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A Security and Privacy Aware Computing Approach on Data Sharing in Cloud Environment by Mustafa Azeez AL Mayyahi, Seyed Amin Hosseini Seno

“…However, the privacy of data is another important challenge that should be considered when transporting, storing, and analyzing data in the public cloud. …”
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Empowering open data sharing for social good: a privacy-aware approach by Tânia Carvalho, Luís Antunes, Cristina Costa Santos, Nuno Moniz

“…Such a sharing process should adhere to state-of-the-art methods in Data Protection by Design and by Default. In this paper, we use a Covid-19 data set from Portugal’s second-largest hospital to show how it is feasible to ensure data privacy while improving the quality and maintaining the utility of the data. …”
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Protection of gait data set for preserving its privacy in deep learning pipeline by Anubha Parashar, Rajveer Singh Shekhawat

“…Such modified data prevent hackers from making use of the data set for adversarial attacks. …”
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Federated learning: a privacy-preserving approach to data-centric regulatory cooperation by Alexander Horst, Paul Loustalot, Sanjeev Yoganathan, Ting Li, Joshua Xu, Weida Tong, David Schneider, Nicolas Löffler-Perez, Erminio Di Renzo, Michael Renaudin

“…Regulatory agencies aim to ensure the safety and efficacy of medical products but often face legal and privacy concerns that hinder collaboration at the data level. …”
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Private detection of relatives in forensic genomics using homomorphic encryption by Fillipe D. M. de Souza, Hubert de Lassus, Ro Cammarota

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Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation by Alper Karamanlıoğlu, Berkan Demirel, Onur Tural, Osman Tufan Doğan, Ferda Nur Alpaslan

“…We evaluate the system through two studies: (1) a benchmark of 750+ USMLE-style questions validating the medical reasoning of fine-tuned LLMs; and (2) a real-world case study (<i>n</i> = 132, 75.8% first-pass agreement) using de-identified MIMIC-III data to assess triage accuracy and responsiveness. …”
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Privacy concerns and avoidance behaviour towards data-driven online behavioural advertising by Shradha Jain, H.C. Purohit

“…Design/methodology/approach – The study employed an online survey method for data collection using a sample size of 345. Reliability and validity of the measurement scales were ensured, and hypotheses developed were tested through PLS-SEM using SMART PLS. …”
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Desensitized Financial Data Generation Based on Generative Adversarial Network and Differential Privacy by Fan Zhang, Luyao Wang, Xinhong Zhang

“…This paper proposes a Noise Visibility Function-Differential Privacy Generative Adversarial Network (NVF-DPGAN) model, which generates privacy preserving data similar to the original data, and can be applied to data augmentation for deep learning. …”
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Internet of things data security and privacy protection based on improved federated learning by Wang Gang

“…At the same time, using the decentralized function to encrypt the privacy of the training model, the learning model can provide more secure and reliable services, aiming to solve the problem of large difference in the quality of computing nodes and data privacy leakage in the current FL. …”
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Federated learning for privacy-enhanced mental health prediction with multimodal data integration by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

“…The proposed framework incorporates a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network within a federated learning environment, ensuring that raw user data remains decentralised and privacy is preserved using differential privacy and encryption techniques. …”
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Privacy-Aware Table Data Generation by Adversarial Gradient Boosting Decision Tree by Shuai Jiang, Naoto Iwata, Sayaka Kamei, Kazi Md. Rokibul Alam, Yasuhiko Morimoto

“…Privacy preservation poses significant challenges in third-party data sharing, particularly when handling table data containing personal information such as demographic and behavioral records. …”
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Legitimate Expectations of Privacy in the Era of Digitalization by E. Ostanina, E. Titova

“…This article contends that in the present era of digitalization people’s right to privacy should be protected no less than it was before the widespread use of digital technologies. …”
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DiffPRFs:random forest under differential privacy by Hai-rong MU, Li-ping DING, Yu-ning SONG, Guo-qing LU

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