The Law Reform Regarding the Regulation of Medical Use of Artificial Intelligence and the Protection of Patient Privacy in the Utilization of Artificial Intelligence in Health Care by Meidiawaty Fusia

“…Further research is needed to determine the protection of patient privacy in the context of the use of artificial intelligence in the health sector and the need for legal reform related to the use of artificial intelligence in the health sector. …”
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Privacy-Preserving Federated Class-Incremental Learning by Jue Xiao, Xueming Tang, Songfeng Lu

“…Additionally, there’s an imperative for client data privacy. Addressing these, we propose the Privacy-Preserving Federated Class-Incremental Learning (PP-FCIL) approach. …”
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Protecting contacts against privacy leaks in smartphones. by Youngrok Cha, Wooguil Pak

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Wasserstein GAN for moving differential privacy protection by Enze Liu, Zhiguang Chu, Xing Zhang

“…Abstract Training machine learning models often requires large datasets, but using sensitive data for training poses risks of privacy leakage. …”
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Privacy protection scheme for mobile social network by Seyyed Mohammad Safi, Ali Movaghar, Mohammad Ghorbani

“…One way to protect one’s privacy is using encryption. Therefore, in the present paper, an improved secure design was presented for mobile social network using ciphertext-policy attribute-based encryption (CP-ABE) and advanced encryption standard (AES) that will encrypt users data in an end-to-end manner. …”
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Privacy-preserving task matching scheme for crowdsourcing by SONG Fuyuan, DING Siyang, WANG Wei, JIANG Qin, FU Zhangjie

“…Due to the potential untrustworthiness of crowdsourcing platforms, which may lead to the leakage of users’ private information, users are required to encrypt their data prior to uploading. To fulfill task matching while preserving privacy, the crowdsourcing platform employs encrypted spatial keyword queries to perform task matching of workers’ interests and locations. …”
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Marketing Implications of Information Society Privacy Concerns by Gheorghe ORZAN, Călin VEGHEŞ, Cătălin SILVESTRU, Mihai ORZAN, Ramona BERE

“…The Internet has facilitated access to intimate user knowledge for both official and private agents and most of modern marketing strategies are based on a wealth of personal user data used to target its specific needs and expectations, a process that has its obvious pros and cons, which have been heavily debated intensively in both academic and social venues. …”
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Privacy-preserving maximum value determination scheme by Min Hou, Min Hou, Yue Wu

“…This paper introduces a quantum-secure scheme for conducting privacy-preserving maximum value determination, allowing the parties to ascertain the highest value from their confidential inputs while keeping non-maximum data private. …”
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An Enhanced Communication Protocol for Location Privacy in WSN by Abdel-Shakour Abuzneid, Tarek Sobh, Miad Faezipour

“…The sensors can sense a phenomenon, which will be represented in a form of data and sent to an aggregator for further processing. …”
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Study on privacy preserving encrypted traffic detection by Xinyu ZHANG, Bingsheng ZHANG, Quanrun MENG, Kui REN

“…Existing encrypted traffic detection technologies lack privacy protection for data and models, which will violate the privacy preserving regulations and increase the security risk of privacy leakage.A privacy-preserving encrypted traffic detection system was proposed.It promoted the privacy of the encrypted traffic detection model by combining the gradient boosting decision tree (GBDT) algorithm with differential privacy.The privacy-protected encrypted traffic detection system was designed and implemented.The performance and the efficiency of proposed system using the CICIDS2017 dataset were evaluated, which contained the malicious traffic of the DDoS attack and the port scan.The results show that when the privacy budget value is set to 1, the system accuracy rates are 91.7% and 92.4% respectively.The training and the prediction of our model is efficient.The training time of proposed model is 5.16 s and 5.59 s, that is only 2-3 times of GBDT algorithm.The prediction time is close to the GBDT algorithm.…”
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Teenagers’ Digital Security: Sociological Analysis by Sergey G. Ushkin, Ekaterina A. Koval, Marina D. Martynova

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Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions by Nisha Thorakkattu Madathil, Fida K. Dankar, Marton Gergely, Abdelkader Nasreddine Belkacem, Saed Alrabaee

“…In general, through systematic categorization and analysis of existing FL systems, we offer insights to design efficient, accurate, and privacy-preserving healthcare applications using cutting-edge FL techniques.…”
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PERSONAL DATA PROTECTION IN CRIMINAL PROCEEDINGS by SMOLKOVA Iraida Vyacheslavovna

“…The most vulnerable to these factors are the privacy and personal data of participants in criminal proceedings. …”
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Self-sovereign management scheme of personal health record with personal data store and decentralized identifier by Tong Min Kim, Taehoon Ko, Byoung Woo Hwang, Hyung Goo Paek, Wan Yeon Lee

“…Conventional personal health record (PHR) management systems are centralized, making them vulnerable to privacy breaches and single points of failure. Despite progress in standardizing healthcare data with the FHIR format, hospitals often lack efficient platforms for transferring PHRs, leading to redundant tests and delayed treatments. …”
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Neurorights, Neurotechnologies and Personal Data: Review of the Challenges of Mental Autonomy by Y. Cornejo

“…Thorough searches were carried out with the search terms “neurotechnology”, “personal data”, “mental privacy”, “neuro-rights”, “neurotechnological interventions”, and “neurotechnological discrimination” on both English and Spanish sites, using search engines like Google Scholar and Redib as well as databases including Scielo, Dialnet, Redalyc, Lilacs, Scopus, Medline, and Pubmed. …”
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When Two are Better Than One: Synthesizing Heavily Unbalanced Data by Francisco Ferreira, Nuno Lourenco, Bruno Cabral, Joao Paulo Fernandes

“…However, they need to fully comply with not only ethical but also regulatory obligations, where, e.g., privacy (strictly) needs to be respected when using or sharing data, thus protecting both the interests of users and organizations. …”
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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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Proposed ethical framework for software requirements engineering by Seblewongel E. Biable, Nuno M. Garcia, Dida Midekso

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Advanced Zero-Shot Learning (AZSL) Framework for Secure Model Generalization in Federated Learning by Muhammad Asif, Surayya Naz, Faheem Ali, Amerah Alabrah, Abdu Salam, Farhan Amin, Faizan Ullah

“…The objective of the proposed model is to increase model generalizability while achieving better performance on various and heterogeneous datasets while preserving data privacy. We have used datasets named MNIST, CIFAR-10, and FEMNIST to measure the model’s accuracy, precision, and generalization capability. …”
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A Systematic Review on the Use of Big Data in Tourism by Jafar Ahangaran, Abbas Sadeghnia

“…Limitations: Although the use of big data in academic research has made significant progress, there are still challenges regarding data quality, data cost, and user privacy concerns. …”
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