Secure, privacy-preserving and federated machine learning in medical imaging

[1]  Aaron Roth,et al.  The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..

[2]  Hao Chen,et al.  CHET: an optimizing compiler for fully-homomorphic neural-network inferencing , 2019, PLDI.

[3]  Keshav Shree Mudgal,et al.  The ethical adoption of artificial intelligence in radiology , 2019, BJR open.

[4]  Dennis Grishin,et al.  Data privacy in the age of personal genomics , 2019, Nature Biotechnology.

[5]  Andreas Theodorou,et al.  Towards ethical and socio-legal governance in AI , 2020, Nature Machine Intelligence.

[6]  Graham Neubig,et al.  Weight Poisoning Attacks on Pretrained Models , 2020, ACL.

[7]  Dan Boneh,et al.  Deriving genomic diagnoses without revealing patient genomes , 2017, Science.

[8]  Michael V. McConnell,et al.  Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning , 2017, Nature Biomedical Engineering.

[9]  Vincent Rijmen,et al.  The Design of Rijndael , 2002, Information Security and Cryptography.

[10]  James Bailey,et al.  Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems , 2019, Pattern Recognit..

[11]  C. Dwork,et al.  Exposed! A Survey of Attacks on Private Data , 2017, Annual Review of Statistics and Its Application.

[12]  Xing Li,et al.  Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks , 2015, Sensors.

[13]  Anders Eklund,et al.  Refacing: Reconstructing Anonymized Facial Features Using Gans , 2018, bioRxiv.

[14]  Hongwei Li,et al.  Secure Multi-Party Computation: Theory, practice and applications , 2019, Inf. Sci..

[15]  Anna Jobin,et al.  The global landscape of AI ethics guidelines , 2019, Nature Machine Intelligence.

[16]  Wojciech M. Czarnecki,et al.  Grandmaster level in StarCraft II using multi-agent reinforcement learning , 2019, Nature.

[17]  Vitaly Shmatikov,et al.  The Tao of Inference in Privacy-Protected Databases , 2018, Proc. VLDB Endow..

[18]  John (Xuefeng) Jiang,et al.  Types of Information Compromised in Breaches of Protected Health Information , 2020, Annals of Internal Medicine.

[19]  Fan Lin,et al.  Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics , 2020, European Radiology.

[20]  Úlfar Erlingsson,et al.  Scalable Private Learning with PATE , 2018, ICLR.

[21]  Kevin S. Chan,et al.  Model poisoning attacks against distributed machine learning systems , 2019, Defense + Commercial Sensing.

[22]  Giuseppe Ateniese,et al.  Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning , 2017, CCS.

[23]  G. Corrado,et al.  End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography , 2019, Nature Medicine.

[24]  Anne F. Kittler,et al.  A cost-benefit analysis of electronic medical records in primary care. , 2003, The American journal of medicine.

[25]  Michael Naehrig,et al.  CryptoNets: applying neural networks to encrypted data with high throughput and accuracy , 2016, ICML 2016.

[26]  Úlfar Erlingsson,et al.  The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks , 2018, USENIX Security Symposium.

[27]  Vitaly Shmatikov,et al.  Robust De-anonymization of Large Sparse Datasets , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).

[28]  David Pointcheval,et al.  Partially Encrypted Machine Learning using Functional Encryption , 2019, NeurIPS 2019.

[29]  Ling Li,et al.  An Instruction Set Architecture for Machine Learning , 2019, ACM Trans. Comput. Syst..

[30]  E. Morris,et al.  Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment. , 2018, Radiology.

[31]  Aseem Rastogi,et al.  CrypTFlow: Secure TensorFlow Inference , 2019, 2020 IEEE Symposium on Security and Privacy (SP).

[32]  Sendhil Mullainathan,et al.  Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People , 2019, FAT.

[33]  Morten Dahl,et al.  Private Machine Learning in TensorFlow using Secure Computation , 2018, ArXiv.

[34]  Eric O. Aboagye,et al.  A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer , 2019, Nature Communications.

[35]  G. Fuller,et al.  Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma , 2019, Nature Communications.

[36]  Daniel L Rubin,et al.  A curated mammography data set for use in computer-aided detection and diagnosis research , 2017, Scientific Data.

[37]  D. Song,et al.  The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Mohammad Al-Rubaie,et al.  Privacy-Preserving Machine Learning: Threats and Solutions , 2018, IEEE Security & Privacy.

[39]  Marcello Ienca,et al.  Artificial Intelligence: the global landscape of ethics guidelines , 2019, ArXiv.

[40]  Peter Richtárik,et al.  Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.

[41]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[42]  Jong Wook Kim,et al.  Privacy-preserving aggregation of personal health data streams , 2018, PloS one.

[43]  Kyungtae Kang,et al.  Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection † , 2017, Sensors.

[44]  Adam D. Smith,et al.  Distributed Differential Privacy via Shuffling , 2018, IACR Cryptol. ePrint Arch..

[45]  Daguang Xu,et al.  Privacy-preserving Federated Brain Tumour Segmentation , 2019, MLMI@MICCAI.

[46]  Hamed Haddadi,et al.  Human-Data Interaction: The Human Face of the Data-Driven Society , 2014, ArXiv.

[47]  Frederik Harder,et al.  Interpretable and Differentially Private Predictions , 2019, AAAI.

[48]  Simson L. Garfinkel,et al.  Issues Encountered Deploying Differential Privacy , 2018, WPES@CCS.

[49]  W. Price,et al.  Privacy in the age of medical big data , 2019, Nature Medicine.

[50]  H Surendra,et al.  A Review Of Synthetic Data Generation Methods For Privacy Preserving Data Publishing , 2017 .

[51]  Bryan Reimer,et al.  MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation , 2017, IEEE Access.

[52]  Ping Wang,et al.  Adversarial Noise Layer: Regularize Neural Network by Adding Noise , 2018, 2019 IEEE International Conference on Image Processing (ICIP).

[53]  Mauro Conti,et al.  A Survey on Homomorphic Encryption Schemes , 2017, ACM Comput. Surv..

[54]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[55]  David S. Melnick,et al.  International evaluation of an AI system for breast cancer screening , 2020, Nature.

[56]  Vladimir Kolesnikov,et al.  A Pragmatic Introduction to Secure Multi-Party Computation , 2019, Found. Trends Priv. Secur..

[57]  James Y. Zou,et al.  Data Shapley: Equitable Valuation of Data for Machine Learning , 2019, ICML.

[58]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[59]  Gaurav Pandey,et al.  Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images , 2019, Scientific Reports.

[60]  R. Braren,et al.  A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy , 2019, PloS one.

[61]  Jiann-Shiun Yuan,et al.  Utilizing Transfer Learning and Homomorphic Encryption in a Privacy Preserving and Secure Biometric Recognition System , 2018, Comput..

[62]  Somesh Jha,et al.  Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.

[63]  Anonymous Pandemic data challenges , 2020, Nature Machine Intelligence.

[64]  Stacy-Ann Elvy,et al.  Paying for Privacy and the Personal Data Economy , 2017 .

[65]  M. Taylor,et al.  Reasonable Expectations of Privacy and Disclosure of Health Data , 2019, Medical law review.

[66]  Arun Rajkumar,et al.  A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty Classification , 2012, AISTATS.

[67]  Y. de Montjoye,et al.  Unique in the shopping mall: On the reidentifiability of credit card metadata , 2015, Science.

[68]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[69]  Corinne Cath Governing artificial intelligence: ethical, legal and technical opportunities and challenges , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[70]  Christopher G Schwarz,et al.  Identification of Anonymous MRI Research Participants with Face-Recognition Software. , 2019, The New England journal of medicine.

[71]  Kevin Nelson,et al.  Evaluating model drift in machine learning algorithms , 2015, 2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA).

[72]  R. Braren,et al.  A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging , 2019, European Radiology Experimental.

[73]  Vitaly Shmatikov,et al.  Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[74]  Kay Hamacher,et al.  Large-Scale Privacy-Preserving Statistical Computations for Distributed Genome-Wide Association Studies , 2018, AsiaCCS.

[75]  B. Malin,et al.  Correction: A Systematic Review of Re-Identification Attacks on Health Data , 2015, PloS one.

[76]  P. Lambin,et al.  Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.

[77]  A. Cavoukian Privacy by Design: Origins, Meaning, and Prospects for Assuring Privacy and Trust in the Information Era , 2012 .