A Symmetric Projection Space and Adversarial Training Framework for Privacy-Preserving Machine Learning with Improved Computational Efficiency by Qianqian Li, Shutian Zhou, Xiangrong Zeng, Jiaqi Shi, Qianye Lin, Chenjia Huang, Yuchen Yue, Yuyao Jiang, Chunli Lv

“…By designing a new projection loss function and combining autoencoders with adversarial training, the proposed method effectively balances privacy protection and model utility. Experimental results show that, for financial time-series data tasks, the model using the projection loss achieves a precision of 0.95, recall of 0.91, and accuracy of 0.93, significantly outperforming the traditional cross-entropy loss. …”
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Fostering social media user intentions: AI-enabled privacy and intrusiveness concerns by Muhammad Haroon Shoukat, Islam Elgammal, Kareem M. Selem, Ali Elsayed Shehata

“…Findings – Privacy concerns significantly affect perceived usefulness. …”
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Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning by Huda Kadhim Tayyeh, Ahmed Sabah Ahmed AL-Jumaili

“…Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major problem. …”
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Evaluating the effectiveness of data governance frameworks in ensuring security and privacy of healthcare data: A quantitative analysis of ISO standards, GDPR, and HIPAA in blockch... by Ameer Ahmed, Asjad Shahzad, Afshan Naseem, Shujaat Ali, Imran Ahmad

“…Although there are existing frameworks to govern healthcare data but they have certain limitations in effectiveness of data governance to ensure security and privacy. …”
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My Holistic Data Share: A WEB3 Data Share Application: Extending Beyond Finance to Privacy-Protected Decentralized Share of Multi-Dimensional Data to Enhance Global Healthcare by SATHYA KRISHNASAMY

“…This paper introduces a concept beyond cryptocurrencies and finance into everyday real-world use cases that need combinatorial access to a person’s holistic data, including financial and health records, genomic data, and advanced directives, among others, that need to be privacy protected and shared with specific actors identified for their roles in the WEB3 ecosystem through decentralized identifiers and non-fungible token badges identifying particular recipients. …”
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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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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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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

“…The hash key method offers significant advantages in reducing blockchain storage costs, enhancing privacy, and maintaining data integrity, but it depends on reliable off-chain storage. …”
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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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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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Sensitive data donation in practice: unforeseen challenges and lessons learned by Alejandra Gómez Ortega

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Enhanced detection of Mpox using federated learning with hybrid ResNet-ViT and adaptive attention mechanisms by V. Maheskumar, R. Vijayarajeswari

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Solving blockchain trilemma using off‐chain storage protocol by Saha Reno, Md. Mokammel Haque

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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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