A Proposed Vision for Using Artificial Intelligence in Enhancing Strategic Value of Human Resources by Nadera Hourani

“…Yet, there are significant challenges in the form of algorithmic bias, data privacy concerns, and organizational readiness. …”
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An Efficient Privacy-preserving Deep Learning Scheme for Medical Image Analysis by J. Andrew Onesimu, J Karthikeyan

“…Data providers morph the images without privacy information using image morphing component. …”
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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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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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A Security and Privacy Aware Computing Approach on Data Sharing in Cloud Environment by Mustafa Azeez AL Mayyahi, Seyed Amin Hosseini Seno

“…Cloud computing has changed the way that organizations manage resources due to their robustness, low cost, and pervasive nature. Data security is usually realized using different methods such as encryption. …”
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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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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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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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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 study conducts experiments using financial data from China Stock Market & Accounting Research (CSMAR) database. …”
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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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Using Homomorphic Proxy Re-Encryption to Enhance Security and Privacy of Federated Learning-Based Intelligent Connected Vehicles by Yang Bai, Yutang Rao, Hongyan Wu, Juan Wang, Wentao Yang, Gaojie Xing, Jiawei Yang, Xiaoshu Yuan

“…These threats can lead to the leakage of sensitive private data, such as location information and driving conditions. …”
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Multiple modes of data sharing can facilitate secondary use of sensitive health data for research by Nicki Tiffin, Themba Mutemaringa, Tsaone Tamuhla, Eddie T Lulamba

“…Increasing volumes of highly granular data provide opportunities to leverage the evidence base, with growing recognition that health data are highly sensitive and onward research use may create privacy issues for individuals providing data. …”
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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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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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