Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities
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E. Jefferson | F. Ritchie | Simon Rogers | Esma Mansouri-Benssassi | S. Reel | Maeve Malone | Jim Q. Smith
[1] Alberto Blanco-Justicia,et al. GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs) , 2022, ArXiv.
[2] Maged N. Kamel Boulos,et al. Privacy-by-Design Environments for Large-Scale Health Research and Federated Learning from Data , 2022, International journal of environmental research and public health.
[3] I. Dayan,et al. Key considerations for the use of artificial intelligence in healthcare and clinical research , 2021, Future Healthcare Journal.
[4] R. Shokri,et al. Enhanced Membership Inference Attacks against Machine Learning Models , 2021, CCS.
[5] Andreas Holzinger,et al. Medical artificial intelligence , 2021, Commun. ACM.
[6] E. Jefferson,et al. A Review of Trusted Research Environments to Support Next Generation Capabilities based on Interview Analysis (Preprint) , 2021, Journal of Medical Internet Research.
[7] Jonathan Waring,et al. Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations , 2021, Informatics.
[8] Andreas Holzinger,et al. Legal aspects of data cleansing in medical AI , 2021, Comput. Law Secur. Rev..
[9] Alastair C. Hume,et al. A National Network of Safe Havens: Scottish Perspective , 2021, Journal of medical Internet research.
[10] Alison Q. O'Neil,et al. Survey: Leakage and Privacy at Inference Time , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Kai Ma,et al. Anomaly Detection for Medical Images Using Self-Supervised and Translation-Consistent Features , 2021, IEEE Transactions on Medical Imaging.
[12] Emiliano De Cristofaro. A Critical Overview of Privacy in Machine Learning , 2021, IEEE Security & Privacy.
[13] Anna Saranti,et al. Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI , 2021, Inf. Fusion.
[14] Ming Y. Lu,et al. Synthetic data in machine learning for medicine and healthcare , 2021, Nature Biomedical Engineering.
[15] Matthias Wilms,et al. An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks , 2021, Sensors.
[16] Daniel Rueckert,et al. End-to-end privacy preserving deep learning on multi-institutional medical imaging , 2021, Nature Machine Intelligence.
[17] J. Zhang,et al. Learning to learn by yourself: Unsupervised meta‐learning with self‐knowledge distillation for COVID‐19 diagnosis from pneumonia cases , 2021, Int. J. Intell. Syst..
[18] Vivek Muthurangu,et al. The role of artificial intelligence in healthcare: a structured literature review , 2021, BMC Medical Informatics and Decision Making.
[19] Michael Backes,et al. Node-Level Membership Inference Attacks Against Graph Neural Networks , 2021, ArXiv.
[20] Colin Raffel,et al. Extracting Training Data from Large Language Models , 2020, USENIX Security Symposium.
[21] J. Tohka,et al. Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) Found by Hierarchical Clustering , 2020, International journal of Alzheimer's disease.
[22] Hercules Dalianis,et al. The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text , 2020, LOUHI.
[23] Giuseppe De Pietro,et al. Reinforcement learning for intelligent healthcare applications: A survey , 2020, Artif. Intell. Medicine.
[24] Jianxin Wang,et al. Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images , 2020, IEEE Journal of Biomedical and Health Informatics.
[25] Olivier Gevaert,et al. Genomic data imputation with variational auto-encoders , 2020, GigaScience.
[26] Gerry Reilly,et al. Trusted Research Environments (TRE) Green Paper , 2020 .
[27] K Rajesh Babu,et al. Brain Tumor segmentation of T1w MRI images based on Clustering using Dimensionality Reduction Random Projection Technique. , 2020, Current medical imaging.
[28] Frederik Barkhof,et al. Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records , 2020, MIE.
[29] Tsan-Ming Choi,et al. Role of Analytics for Operational Risk Management in the Era of Big Data , 2020, Decis. Sci..
[30] Ziqian Xie,et al. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction , 2020, npj Digital Medicine.
[31] Mingfu Xue,et al. Machine Learning Security: Threats, Countermeasures, and Evaluations , 2020, IEEE Access.
[32] Qiang Zhang,et al. Classification Model on Big Data in Medical Diagnosis Based on Semi-Supervised Learning , 2020, Comput. J..
[33] D. Camarillo,et al. Problems in pregnancy, modeling fetal mortality through the Naïve Bayes classifier , 2020 .
[34] Kayla A Johnson,et al. Supervised learning is an accurate method for network-based gene classification , 2020, Bioinformatics.
[35] Xiaolei Xie,et al. Predicting Hospital Readmission: A Joint Ensemble-Learning Model , 2020, IEEE Journal of Biomedical and Health Informatics.
[36] Bjoern H. Menze,et al. Deep Reinforcement Learning for Organ Localization in CT , 2020, MIDL.
[37] Peter B. Walker,et al. Federated Learning for Healthcare Informatics , 2019, Journal of Healthcare Informatics Research.
[38] Georgia Tourassi,et al. Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records. , 2019, Cancer research.
[39] Xue Ying,et al. An Overview of Overfitting and its Solutions , 2019, Journal of Physics: Conference Series.
[40] Joe Naoum-Sawaya,et al. Optimization Models for Machine Learning: A Survey , 2019, Eur. J. Oper. Res..
[41] Fredrik D. Johansson,et al. Guidelines for reinforcement learning in healthcare , 2019, Nature Medicine.
[42] Amir Houmansadr,et al. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning , 2018, 2019 IEEE Symposium on Security and Privacy (SP).
[43] Fei Wang,et al. Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..
[44] Nikita Borisov,et al. Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations , 2018, CCS.
[45] Mustafa Musa Jaber,et al. Cloud based framework for diagnosis of diabetes mellitus using K-means clustering , 2018, Health Information Science and Systems.
[46] Michael Veale,et al. Algorithms that remember: model inversion attacks and data protection law , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[47] O. Obulesu,et al. Machine Learning Techniques and Tools: A Survey , 2018, 2018 International Conference on Inventive Research in Computing Applications (ICIRCA).
[48] Mario Fritz,et al. ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models , 2018, NDSS.
[49] Edward Y. Chang,et al. Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning , 2018, AAAI.
[50] Carlos R. García-Alonso,et al. Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning , 2018, Health Research Policy and Systems.
[51] Vitaly Shmatikov,et al. Chiron: Privacy-preserving Machine Learning as a Service , 2018, ArXiv.
[52] Jiachen Yang,et al. Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis , 2018, ArXiv.
[53] Reza Shokri,et al. Machine Learning with Membership Privacy using Adversarial Regularization , 2018, CCS.
[54] Ajmal Mian,et al. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.
[55] M. Goddard. The EU General Data Protection Regulation (GDPR): European Regulation that has a Global Impact , 2017 .
[56] Vitaly Shmatikov,et al. Machine Learning Models that Remember Too Much , 2017, CCS.
[57] F. Ritchie. The ‘Five Safes’: A framework for planning, designing and evaluating data access solutions , 2017 .
[58] Haipeng Shen,et al. Artificial intelligence in healthcare: past, present and future , 2017, Stroke and Vascular Neurology.
[59] Masaki Kobayashi,et al. Usefulness of a decision tree model for the analysis of adverse drug reactions: Evaluation of a risk prediction model of vancomycin‐associated nephrotoxicity constructed using a data mining procedure , 2017, Journal of evaluation in clinical practice.
[60] D. Adkins,et al. Machine Learning and Electronic Health Records: A Paradigm Shift. , 2017, The American journal of psychiatry.
[61] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[62] Vitaly Shmatikov,et al. Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[63] M. Emre Celebi,et al. Unsupervised Learning Algorithms , 2016 .
[64] Hsin-Min Lu,et al. Modeling healthcare data using multiple-channel latent Dirichlet allocation , 2016, J. Biomed. Informatics.
[65] Svetha Venkatesh,et al. DeepCare: A Deep Dynamic Memory Model for Predictive Medicine , 2016, PAKDD.
[66] Somesh Jha,et al. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.
[67] Rina Mishra,et al. A review on steganography and cryptography , 2015, 2015 International Conference on Advances in Computer Engineering and Applications.
[68] Anthony N. Nguyen,et al. Active learning: a step towards automating medical concept extraction , 2015, J. Am. Medical Informatics Assoc..
[69] Somesh Jha,et al. Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing , 2014, USENIX Security Symposium.
[70] D. Zehnder,et al. PROBLEMS IN PREGNANCY , 2011, BMJ : British Medical Journal.
[71] Amine Nait-Ali,et al. Hidden biometrics: Towards using biosignals and biomedical images for security applications , 2011, International Workshop on Systems, Signal Processing and their Applications, WOSSPA.
[72] R. Siezen,et al. others , 1999, Microbial Biotechnology.
[73] Asif Karim,et al. Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques , 2021, IEEE Access.
[74] Wangrok Oh,et al. Measurement and Analysis of Human Body Channel Response for Biometric Recognition , 2021, IEEE Transactions on Instrumentation and Measurement.
[75] Uno Fors,et al. De-identification of Clinical Text for Secondary Use: Research Issues , 2021, HEALTHINF.
[76] H. Dalianis,et al. Are Clinical BERT Models Privacy Preserving? The Difficulty of Extracting Patient-Condition Associations , 2021, HUMAN@AAAI Fall Symposium.
[77] Evert-Ben van Veen,et al. The Ten Commandments of Ethical Medical AI , 2021, Computer.
[78] Jupeng Li,et al. End-to-End Coordinate Regression Model with Attention-Guided Mechanism for Landmark Localization in 3D Medical Images , 2020, MLMI@MICCAI.
[79] Sundaramoorthy Selvaperumal,et al. Privacy Protection of Patient Medical Images using Digital Watermarking Technique for E-healthcare System. , 2019, Current medical imaging reviews.