Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy.

[1]  S. Ourselin,et al.  Brain Imaging Generation with Latent Diffusion Models , 2022, DGM4MICCAI@MICCAI.

[2]  Sai Sree Laya Chukkapalli,et al.  PriveTAB: Secure and Privacy-Preserving sharing of Tabular Data , 2022, IWSPA@CODASPY.

[3]  B. Ommer,et al.  High-Resolution Image Synthesis with Latent Diffusion Models , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Fang Wen,et al.  Vector Quantized Diffusion Model for Text-to-Image Synthesis , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Amirarsalan Rajabi,et al.  TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks , 2021, Mach. Learn. Knowl. Extr..

[6]  Stefano Ermon,et al.  CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation , 2021, NeurIPS.

[7]  Prafulla Dhariwal,et al.  Diffusion Models Beat GANs on Image Synthesis , 2021, NeurIPS.

[8]  Robert Birke,et al.  CTAB-GAN: Effective Table Data Synthesizing , 2021, ACML.

[9]  A. Tucker,et al.  Generating and evaluating cross‐sectional synthetic electronic healthcare data: Preserving data utility and patient privacy , 2021, Comput. Intell..

[10]  Edward A. Fox,et al.  Differentially Private Synthetic Medical Data Generation using Convolutional GANs , 2020, Inf. Sci..

[11]  Jaehyeon Kim,et al.  HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis , 2020, NeurIPS.

[12]  Linda Coyle,et al.  Generation and evaluation of synthetic patient data , 2020, BMC Medical Research Methodology.

[13]  Jean-Pierre Briot,et al.  From artificial neural networks to deep learning for music generation: history, concepts and trends , 2020, Neural Computing and Applications.

[14]  Thomas Steinke,et al.  The Discrete Gaussian for Differential Privacy , 2020, NeurIPS.

[15]  S. Kalkman,et al.  Patients’ and public views and attitudes towards the sharing of health data for research: a narrative review of the empirical evidence , 2019, Journal of Medical Ethics.

[16]  Magnus Wiese,et al.  Quant GANs: deep generation of financial time series , 2019, Quantitative Finance.

[17]  Lei Xu,et al.  Modeling Tabular data using Conditional GAN , 2019, NeurIPS.

[18]  Junqiao Chen,et al.  The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures , 2019, BMC Medical Informatics and Decision Making.

[19]  Chao-Lin Liu,et al.  Synthesizing electronic health records using improved generative adversarial networks , 2018, J. Am. Medical Informatics Assoc..

[20]  K. Hood,et al.  Challenges in accessing routinely collected data from multiple providers in the UK for primary studies: Managing the morass. , 2018, International journal of population data science.

[21]  Assefaw H. Gebremedhin,et al.  Synthetic Sensor Data Generation for Health Applications: A Supervised Deep Learning Approach , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[22]  Sushil Jajodia,et al.  Data Synthesis based on Generative Adversarial Networks , 2018, Proc. VLDB Endow..

[23]  Ashish Khetan,et al.  PacGAN: The Power of Two Samples in Generative Adversarial Networks , 2017, IEEE Journal on Selected Areas in Information Theory.

[24]  D. Rubin,et al.  Multiply-Imputed Synthetic Data: Advice to the Imputer , 2017 .

[25]  Cecilia M. Procopiuc,et al.  PrivBayes , 2017 .

[26]  Mark Kramer,et al.  Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record , 2017, J. Am. Medical Informatics Assoc..

[27]  Emiliano De Cristofaro,et al.  LOGAN: Membership Inference Attacks Against Generative Models , 2017, Proc. Priv. Enhancing Technol..

[28]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[29]  Jimeng Sun,et al.  Generating Multi-label Discrete Patient Records using Generative Adversarial Networks , 2017, MLHC.

[30]  Ilya Mironov,et al.  Rényi Differential Privacy , 2017, 2017 IEEE 30th Computer Security Foundations Symposium (CSF).

[31]  Dimitris N. Metaxas,et al.  StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[32]  Xinping Guan,et al.  Differential Private Noise Adding Mechanism and Its Application on Consensus Algorithm , 2016, IEEE Transactions on Signal Processing.

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

[34]  Ian Goodfellow,et al.  Deep Learning with Differential Privacy , 2016, CCS.

[35]  Jorge Cortés,et al.  Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design , 2015, Autom..

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

[37]  Zoubin Ghahramani,et al.  Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data , 2015, ICML.

[38]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

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

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

[41]  P. Dagnelie,et al.  The Maastricht Study: an extensive phenotyping study on determinants of type 2 diabetes, its complications and its comorbidities , 2014, European Journal of Epidemiology.

[42]  Ruth Gilbert,et al.  Accessing electronic administrative health data for research takes time , 2013, Archives of Disease in Childhood.

[43]  Geir E. Dullerud,et al.  Differentially private iterative synchronous consensus , 2012, WPES '12.

[44]  Karl Pearson F.R.S. X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling , 2009 .

[45]  Cynthia Dwork,et al.  Differential Privacy: A Survey of Results , 2008, TAMC.

[46]  John R. Hershey,et al.  Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[47]  Moni Naor,et al.  Our Data, Ourselves: Privacy Via Distributed Noise Generation , 2006, EUROCRYPT.

[48]  David B Resnik,et al.  Openness versus Secrecy in Scientific Research , 2006, Episteme.

[49]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[50]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[51]  Susan Mckeever,et al.  Synthesising Tabular Data using Wasserstein Conditional GANs with Gradient Penalty (WCGAN-GP) , 2020, AICS.

[52]  Eenjun Hwang,et al.  Conditional Tabular GAN-Based Two-Stage Data Generation Scheme for Short-Term Load Forecasting , 2020, IEEE Access.

[53]  Meera Narvekar,et al.  Evolution of Neural Text Generation: Comparative Analysis , 2020 .

[54]  Andrew L. Maas Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[55]  Lawrence O. Gostin,et al.  The Value, Importance, and Oversight of Health Research , 2009 .

[56]  J. Presedo,et al.  Comparison of different methods for hemodialysis evaluation by means of ROC curves: from artificial intelligence to current methods. , 2005, Clinical nephrology.

[57]  Jerome P. Reiter,et al.  Multiple Imputation for Statistical Disclosure Limitation , 2003 .