Search Results for author:
Found 9 papers, 3 papers with code
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks.
Enabling Trade-offs in Privacy and Utility in Genomic Data Beacons and Summary Statistics
Several approaches have been proposed to preserve privacy, which either suppress a subset of genomic variants or modify query responses for specific variants (e. g., adding noise, as in differential privacy).
Risk of re-identification for shared clinical speech recordings
Risk is high for a small search space but drops as the search space grows ($precision >0. 85$ for $<1*10^{6}$ comparisons, $precision <0. 5$ for $>3*10^{6}$ comparisons).
A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models
The results illustrate that there is a utility-privacy tradeoff for sharing synthetic EHR data.
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.
Re-identification of Individuals in Genomic Datasets Using Public Face Images
However, recent studies have suggested that genomic data can be effectively matched to high-resolution three-dimensional face images, which raises a concern that the increasingly ubiquitous public face images can be linked to shared genomic data, thereby re-identifying individuals in the genomic data.
GINN: Fast GPU-TEE Based Integrity for Neural Network Training
In this work, we focus on the setting where the integrity of the outsourced Deep Learning (DL) model training is ensured by TEE.
Generating Electronic Health Records with Multiple Data Types and Constraints
Sharing electronic health records (EHRs) on a large scale may lead to privacy intrusions.
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational phenotyping.
