Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy by Ansam Khraisat, Ammar Alazab, Moutaz Alazab, Areej Obeidat, Sarabjot Singh, Tony Jan

“…While allowing devices to benefit from the network’s collective insights, this decentralised learning methodology safeguards data privacy. Using a real-world IoT dataset and two popular federated learning algorithms—Federated Average and Federated Average with Momentum—the study assesses the effectiveness of PEIoT-DS. …”
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Classification auditting scheme for privacy protection of outsourced EHR based on distributed storage by ZHANG Xiaoxu, CHEN Yuchen, HA Guanxiong, JIA Chunfu

“…EHR contains a large amount of private information, however, data outsourcing can create security risks, potentially compromising the integrity and privacy of EHR. …”
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Classification auditting scheme for privacy protection of outsourced EHR based on distributed storage by ZHANG Xiaoxu, CHEN Yuchen, HA Guanxiong, JIA Chunfu

“…EHR contains a large amount of private information, however, data outsourcing can create security risks, potentially compromising the integrity and privacy of EHR. …”
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Privacy‐preserving speaker verification system based on binary I‐vectors by Aymen Mtibaa, Dijana Petrovska‐Delacrétaz, Jérôme Boudy, Ahmed Ben Hamida

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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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Blockchain-Based Privacy-Preserving Authentication and Access Control Model for E-Health Users by Abdullah Alabdulatif

“…The advancement of e-health systems has resulted in substantial enhancements in healthcare delivery via effective data management and accessibility. The use of digital health solutions presents dangers to sensitive health information, including unauthorised access, privacy violations, and security weaknesses. …”
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An enhanced dynamic KC-slice model for privacy preserving data publishing with multiple sensitive attributes by inducing sensitivity by N.V.S. Lakshmipathi Raju, M.N. Seetaramanath, P. Srinivasa Rao

“…Privacy Preserving Data Publishing (PPDP) is an important aspect of real world scenarios. …”
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Board of Directors Role in Data Privacy Governance: Making the Transition from Compliance Driven to Good Business Stewardship by David Warner, Lisa McKee

“…Data collection, use, leveraging, and sharing as a business practice and advantage has proliferated over the past decade. …”
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Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes by J. Jayapradha, M. Prakash, Youseef Alotaibi, Osamah Ibrahim Khalaf, Saleh Ahmed Alghamdi

“…Currently, significant focus has been established to protect privacy during data publishing. The existing privacy models for multiple sensitive attributes do not concentrate on the correlation among the attributes, which in turn leads to much utility loss. …”
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Sensitive data donation in practice: unforeseen challenges and lessons learned by Alejandra Gómez Ortega

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Efficient Traceability Systems with Smart Contracts: Balancing On-Chain and Off-Chain Data Storage for Enhanced Scalability and Privacy by Manuel José Fernández-Iglesias, Christian Delgado von Eitzen, Luis Anido-Rifón

“…We discuss the advantages of using blockchain technology to notarise traceability activities, ensuring immutability and transparency by exploring two main scenarios, namely one where hash keys (i.e, cryptographic digests) are stored on-chain while detailed data remain off-chain, and another where all traceability data are fully stored on-chain. …”
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An online tool based on the Internet of Things and intelligent blockchain technology for data privacy and security in rural and agricultural development by Krishnaprasath Vellimalaipattinam Thiruvenkatasamy, Hayder M. A. Ghanimi, Sudhakar Sengan, Meshal Ghalib Alharbi

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Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals by Tsung-Ting Kuo, Rodney A. Gabriel, Jejo Koola, Robert T. Schooley, Lucila Ohno-Machado

“…We introduce Distributed Cross-Learning for Equitable Federated models (D-CLEF), which incorporates horizontally- or vertically-partitioned data without disseminating patient-level records, to protect patients’ privacy. …”
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The method for approximate continuous Skyline query based on privacy protection in road network by Song Li, Xiaolong Yang, Liping Zhang, Guanglu Sun

“…Abstract Existing road network continuous Skyline queries often neglect user location privacy and data privacy protection. This paper proposes a method for Approximate Continuous Skyline Query with Privacy Protection in road network (ACPP) environments to address these issues. …”
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Design of an efficient dynamic context‐based privacy policy deployment model via dual bioinspired Q learning optimisations by Namrata Jiten Patel, Ashish Jadhav

“…The combination of ALO, FFO, and Q‐learning techniques offers a practical solution to evolving data privacy challenges and enhances flexibility in various use case scenarios.…”
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Federated intelligence for smart grids: a comprehensive review of security and privacy strategies by Raseel Z. Alshamasi, Dina M. Ibrahim

“…This review paper critically surveys recent advancements in federated learning (FL) as a privacy-preserving machine learning technique for addressing these challenges. …”
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Enhancing Privacy While Preserving Context in Text Transformations by Large Language Models by Tymon Lesław Żarski, Artur Janicki

“…Despite the convenience, many users are unaware of the risks posed to their sensitive and personal data. This study addresses this issue by presenting a comprehensive solution to prevent personal data leakage using online tools. …”
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Mining privacy-preserving association rules using transaction hewer allocator and facile hash algorithm in multi-cloud environments by D. Dhinakaran, S. Gopalakrishnan, D. Selvaraj, M.S. Girija, G. Prabaharan

“…However, in outsourced data for tasks like frequent itemset mining, privacy is an important issue. …”
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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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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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