Privacy-preserving multi-party data joint analysis scheme based on multiset operations in smart contracts by Xin Liu, Huize Gao, Dan Luo, Lanying Liang, Lu Peng, Yuchen Zhang, Shijie Jia, Gang Xu, Xiubo Chen, Baohua Zhang, Yu Gu

“…However, the demand for multi-party data joint analysis within these contracts faces challenges of privacy leakage and malicious deception. …”
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Enhanced Consumer Healthcare Data Protection Through AI-Driven TinyML and Privacy-Preserving Techniques by S. Aanjankumar, Monoj Kumar Muchahari, Shabana Urooj, Ishmeet Kaur, Rajesh Kumar Dhanaraj, Hanan Abdullah Mengash, S. Poonkuntran, Parag Ravikant Kaveri

“…The proposed method with TinyML looks at patient data, like ECG readings and reports of unusual heartbeats, right on local edge devices that have limited resources, enabled immediate privacy checks while using minimal computing power. …”
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Clinical Simulation Program for the Training of Health Profession Residents in Confidentiality and the Use of Social Networks by Alejandro Martínez-Arce, Alberto Bermejo-Cantarero, Laura Muñoz de Morales-Romero, Víctor Baladrón-González, Natalia Bejarano-Ramírez, Gema Verdugo-Moreno, María Antonia Montero-Gaspar, Francisco Javier Redondo-Calvo

“…After the course, the participants reported intending to modify their behavior when sharing patient data without their consent and with respect to how patients are informed; Conclusions: The use of advanced simulation in the training of interprofessional teams of residents is as an effective tool for initiating attitudinal change and increasing knowledge related to patient privacy and confidentiality. …”
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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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Securing face images in UAV networks using chaos and DNA cryptography approach by Saba Inam, Shamsa Kanwal, Eaman Amin, Omar Cheikhrouhou, Monia Hamdi

“…Abstract In future applications of UAVs, the frequent transmission of image data raises significant security concerns, particularly regarding the privacy of facial information. …”
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A clustering-based approach for classifying data streams using graph matching by Yuxin Du, Mingshu He, Xiaojuan Wang

“…Abstract In response to challenges such as data encryption, uneven distribution, and user privacy concerns in network traffic classification, this paper presents a clustering-based approach.In response to challenges such as data encryption, uneven distribution, and user privacy concerns in network traffic classification, this paper presents a clustering-based approach. …”
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A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference by Sadhana Selvakumar, B. Senthilkumar

“…However, sharing sensitive raw medical data with third parties for analysis raises significant privacy concerns. …”
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Synthetic healthcare data utility with biometric pattern recognition using adversarial networks by Adil O. Khadidos, Hariprasath Manoharan, Alaa O. Khadidos, Shitharth Selvarajan, Subhav Singh

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Risk assessment of data protection in the maritime industry using system-theoretic process analysis by Sutthipong Yungratog, Hyungju Kim, Wonsiri Punurai, Sotarat Thammaboosadee

“…Data protection assessment is a significant issue to security and privacy enhancement. …”
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Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions by R. Vijay Anand, G. Magesh, I. Alagiri, Madala Guru Brahmam, Balamurugan Balusamy, Chithirai Pon Selvan, Haya Mesfer Alshahrani, Masresha Getahun, Ben Othman Soufiene

“…After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. …”
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Secure and trusted data sharing in smart healthcare using blockchain and IoT integration by Soubhagya Ranjan Mallick, Srichandan Sobhanayak, Rakesh Kumar Lenkar

“…A device proxy monitors potentially vulnerable IoT devices and uses cryptography to ensure that data remains private. …”
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Effective deep learning aided vehicle classification approach using Seismic Data by Sherief Hashima, Mohamed H. Saad, Ahmad B. Ahmad, Takeshi Tsuji, Hamada Rizk

“…Our approach achieves an impressive accuracy of 99.8%, underscoring its potential for robust and precise VC in ITSs using seismic data, particularly in data-scarce scenarios. …”
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ECDSA-based tamper detection in medical data using a watermarking technique by Rupa Ch, Naga Vivek K, Gautam Srivastava, Reddy Gadekallu

“…Telemedicine is a form of healthcare delivery that employs communication technology to provide medical care to patients remotely. The use of telemedicine has seen a significant increase in recent years, presenting challenges such as patient privacy, data security, the need for reliable communication technology, and the potential for misdiagnosis without a physical examination. …”
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PK-Judge: Enhancing IP Protection of Neural Network Models Using an Asymmetric Approach by Wafaa Kanakri, Brian King

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Community views on the secondary use of general practice data: Findings from a mixed‐methods study by Annette J. Braunack‐Mayer, Carolyn Adams, Alberto Nettel‐Aguirre, Belinda Fabrianesi, Lucy Carolan, Justin Beilby, Felicity Flack

“…Abstract Introduction General practice data, particularly when combined with hospital and other health service data through data linkage, are increasingly being used for quality assurance, evaluation, health service planning and research. …”
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Federated Learning Model Using CBDPL for Medical Image Segmentation by K. Hemalatha, Shradhanjali Das

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Efficient Keyset Design for Neural Networks Using Homomorphic Encryption by Youyeon Joo, Seungjin Ha, Hyunyoung Oh, Yunheung Paek

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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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