Comprehensive Review and Analysis of Cryptography Techniques in Cloud Computing by K. Sasikumar, Sivakumar Nagarajan

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Federated Learning for Heterogeneous Multi-Site Crop Disease Diagnosis by Wesley Chorney, Abdur Rahman, Yibin Wang, Haifeng Wang, Zhaohua Peng

“…The objective of this collaboration is to create a classifier that every farm can use to detect and manage rice crop diseases by leveraging data sharing while safeguarding data privacy. …”
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A Systematic Literature Review of Machine Unlearning Techniques in Neural Networks by Ivanna Daniela Cevallos, Marco E. Benalcázar, Ángel Leonardo Valdivieso Caraguay, Jonathan A. Zea, Lorena Isabel Barona-López

“…This review examines the field of machine unlearning in neural networks, an area driven by data privacy regulations such as the General Data Protection Regulation and the California Consumer Privacy Act. …”
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Factors influencing patients’ willingness to share their digital health data for primary and secondary use: A theory- and evidence-based overview of reviews by Sabrina Fesl, Caroline Lang, Jochen Schmitt, Stefanie Brückner, Stephen Gilbert, Stefanie Deckert, Madlen Scheibe

“…SRs underwent a multistage screening process using the inclusion and exclusion criteria based on the Population, Concept/Construct, and Context (PCC) framework, followed by data extraction and quality assessment using revised measurement tool to assess systematic reviews (R-AMSTAR2). …”
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Concurrent prescriptions for opioids and benzodiazepines and risk of opioid overdose: protocol for a retrospective cohort study using linked administrative data by Kristian B Filion, Robyn Tamblyn, David L Buckeridge, Erin Y Liu

“…Our aim was to assess the risk of opioid overdose associated with concurrent use of opioids and benzodiazepines relative to opioids alone.Methods and analysis A retrospective cohort study will be conducted using medical claims data from adult residents of Montréal, Canada. …”
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Ethereum blockchain for electronic health records: securing and streamlining patient management by J. S. Simi Mole, J. S. Simi Mole, R. S. Shaji, R. S. Shaji

“…Electronic health records (EHRs) are increasingly replacing traditional paper-based medical records due to their speed, security, and ability to eliminate redundant data. However, challenges such as EHR interoperability and privacy concerns remain unresolved. …”
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Convergence Analysis for Differentially Private Federated Averaging in Heterogeneous Settings by Yiwei Li, Shuai Wang, Qilong Wu

“…Federated learning (FL) has emerged as a prominent approach for distributed machine learning, enabling collaborative model training while preserving data privacy. However, the presence of non-i.i.d. data and the need for robust privacy protection introduce significant challenges in theoretically analyzing the performance of FL algorithms. …”
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Patient and clinician opinions on internet search data use in therapy: Insights, considerations, and guidelines for integrating a new digital phenotyping measure by Alex Dhima, Soumya Choudhary, Keris Myrick, John Torous

“…More granular search data in visualizations was considered more useful when compared to vague search frequencies. …”
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Lessons Learned From European Health Data Projects With Cancer Use Cases: Implementation of Health Standards and Internet of Things Semantic Interoperability by Amelie Gyrard, Somayeh Abedian, Philip Gribbon, George Manias, Rick van Nuland, Kurt Zatloukal, Irina Emilia Nicolae, Gabriel Danciu, Septimiu Nechifor, Luis Marti-Bonmati, Pedro Mallol, Stefano Dalmiani, Serge Autexier, Mario Jendrossek, Ioannis Avramidis, Eva Garcia Alvarez, Petr Holub, Ignacio Blanquer, Anna Boden, Rada Hussein

“…These initiatives aim to enhance the analysis of heterogeneous health data while aligning with EHDS implementation, specifically for the EHDS for the secondary use of data (EHDS2). …”
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DEBPIR: enhancing information privacy in decentralized business modeling by Gulshan Kumar, Rahul Saha, Mauro Conti, Tai Hoon Kim

“…Abstract Business modelling often involves extensive data collection and analysis, raising concerns about privacy infringement. …”
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A proxy for privacy uncovering the surveillance ecology of mobile apps by Signe Sophus Lai, Sofie Flensburg

“…We explore the surveillance ecology of mobile apps and thereby the privacy implications of everyday smartphone use through three analytical perspectives: The first focuses on the ‘appscapes’ of individual smartphone users and investigates the consequences of which and how many mobile apps users download on their phones; the second compares different types of apps in order to study the app ecology and the relationships between app and third-party service providers; and the third focuses on a particular app category and discusses the functional as well as the commercial incentives for permissions and third-party collaborations. …”
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Financial equality through technology: Do perceived risks deter Indian women from sustained use of mobile payment services? by Priyanka Yadav, Abhishek Kumar, Saroj Kumar Mishra, Khyati Kochhar

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Using fuzzy decision support to create a positive mental health environment for preschoolers by Xinyue Li

“…To assess their mental health before starting school, preschoolers need early detection, intervention, and assessment. However, data shortages, heterogeneity, privacy issues, model interpretability, and generalization restrictions hamper the review process. …”
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Impact of Normalization Techniques on Synthetic Load Profile Generation Using Deep Generative Models by Luis H. T. Bandoria, Walquiria N. Silva, Madson C. De Almeida

“…However, the impact of data normalization on their performance remains insufficiently explored. …”
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Privacy computing:concept, connotation and its research trend by Feng-hua LI, Hui LI, Yan JIA, Neng-hai YU, Jian WENG

“…s: With the widespread application of mobile Internet, cloud computing and big data technologies, people enjoy the convenience of electronic business, information retrieval, social network and other services, whereas the threats of privacy leaks are ever growing due to the use of big data analytics. …”
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Providing Privacy Protection and Personalization Awareness for Android Devices by Hongliang Liang, Dongyang Wu, Shirun Liu, Hao Dai, Haifeng Liu

“…As a result, the risk of users compromising their privacy has risen exponentially. Mobile users currently cannot control how various applications handle the privacy of their sensor data. …”
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Customer adoption of smartwatches – a privacy calculus perspective by Ajay Kumar, Parvez Ahmad, Daruri Venkata Srinivas Kumar, Megha Gupta

“…The authors collected 310 responses using a structured questionnaire; after data cleaning, 270 responses were used for data analysis. …”
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Survey on vertical federated learning: algorithm, privacy and security by Jinyin CHEN, Rongchang LI, Guohan HUANG, Tao LIU, Haibin ZHENG, Yao CHENG

“…., model parameters, parameter gradients, embedding representation, etc.） applied to data distributed across various institutions.FL reduces the risk of privacy leakage, since raw data is not allowed to leave the institution.According to the difference in data distribution between institutions, FL is usually divided into horizontal federated learning （HFL）, vertical federated learning （VFL）, and federal transfer learning （TFL）.VFL is suitable for scenarios where institutions have the same sample space but different feature spaces and is widely used in fields such as medical diagnosis, financial and security of VFL.Although VFL performs well in real-world applications, it still faces many privacy and security challenges.To the best of our knowledge, no comprehensive survey has been conducted on privacy and security methods.The existing VFL was analyzed from four perspectives: the basic framework, communication mechanism, alignment mechanism, and label processing mechanism.Then the privacy and security risks faced by VFL and the related defense methods were introduced and analyzed.Additionally, the common data sets and indicators suitable for VFL and platform framework were presented.Considering the existing challenges and problems, the future direction and development trend of VFL were outlined, to provide a reference for the theoretical research of building an efficient, robust and safe VFL.…”
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Automated redaction of names in adverse event reports using transformer-based neural networks by Eva-Lisa Meldau, Shachi Bista, Carlos Melgarejo-González, G. Niklas Norén

“…Because the Yellow Card data contained few names, we used predictive models to select narratives for training. …”
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A Survey on Privacy-Preserving Machine Learning Inference by Stanisław Barański

“…Use cases in healthcare, finance, and education show how these techniques balance privacy with practical performance. …”
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