Privacy-Preserving Distributed Deep Learning for Clinical Data

By Brett K. Beaulieu-Jones, William Yuan, Samuel G. Finlayson, Zhiwei Steven Wu. 2018

52 citations recorded by the source corpus.

Deep learning with medical data often requires larger samples sizes than are available at single providers. While data sharing among institutions is desirable to train more accurate and sophisticated models, it can lead to severe privacy concerns due the sensitive nature of the data. This problem has motivated a number of studies on distributed training of neural networks that do not require direct sharing of the training data. However, simple distributed training does not offer provable privacy guarantees to satisfy technical safe standards and may reveal information about the underlying patients. We present a method to train neural networks for clinical data in a distributed fashion under differential privacy. We demonstrate these methods on two datasets that include information from multiple independent sites, the eICU collaborative Research Database and The Cancer Genome Atlas.

Subfields: Federated & Distributed Learning, Privacy & Security, Medical & Health AI

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Citations in the corpus

Privacy in Deep Learning: A Survey (2020, methodology): “w/ DNN Distributed DP [87] # # eICU, TCGA Classification w/ DNN DP model publishing [88] # # MNIST, CIFAR Image Classification w/ DNN DP federated learning [89] # # MNIST Image Classification w/ DNN ScalarDP, PrivUnit [90] # # MNIST, CIFAR Image Classification w/ DNN DSSGD [21] # # MNIST, SVHN Image”
When Relaxations Go Bad: "Differentially-Private" Machine Learning (2019, background): “[5] Deep Learning MA eICU 4,328 11 2 3.”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, unknown): “Similarly, the authors in [263] worked over”
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? (2021, background): “While one may mitigate concerns by attempting to remove PHI from datasets, no approach will be perfect (Beaulieu-Jones et al., 2018; Johnson et al., 2020).”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, methodology): “privacy mechanisms [133], [134]. For instance, privacy-preserving dis13 tributed DL for clinical data using differential privacy that incorporates the idea of cyclical weight transfer is presented in [135]. 3) Federated Learning: The idea of federated learning (FL) has been recently proposed by Goog”
Think Locally, Act Globally: Federated Learning with Local and Global Representations (2020, background): “Differential privacy has also been integrated with deep learning [1], distributed learning [2, 79, 7, 18, 35], and federated learning [29, 73, 28].”