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Next-Generation Capabilities in Trusted Research Environments: Interview Study
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
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Reconstruction Attacks Against Mobile-Based Continuous Authentication Systems in the Cloud
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Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
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Federated Learning With Differential Privacy: Algorithms and Performance Analysis
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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
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Efficiently Stealing your Machine Learning Models
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Exposed! A Survey of Attacks on Private Data
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Imaging-Genomics Databases
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Recent methodological advances in federated learning for healthcare
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The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling
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The Melbourne Institute Data Lab, a Secure Access Environment for Informing Future Social and Economic Policy
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A Clinician's Guide to Sharing Data for AI in Ophthalmology
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inferred← citeshttps://doi.org/10.23889/ijpds.v8i1.2165openalex-snapshot
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inferred← citeshttps://doi.org/10.31219/osf.io/hj2sgopenalex-snapshot
OpenSAFELY: a platform for analysing electronic health records designed for reproducible research
inferred← citeshttps://doi.org/10.3389/fdata.2024.1266031openalex-snapshot
Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project
inferred← citeshttps://doi.org/10.3389/fdgth.2025.1644291openalex-snapshot
Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities
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Advancing precision oncology with AI-powered genomic analysis
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Population health management genomic new-born screens and multi-omics intercepts
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Ethical Considerations for Machine Learning Research Using Free-Text Electronic Medical Records: Challenges, Evidence, and Best Practices
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