# Supplement: SCH: Enabling Data Outsourcing and Sharing for AI-powered Parkinson's Research

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2022 · $281,290

## Abstract

Supplement Project Summary
What does Project R01 LM014027-01 do? Artiﬁcial intelligence holds the promise of transforming
data-driven biomedical research and computational health informatics for more accurate diagnosis and
better treatment at lower cost. In the meantime, modern digital and mobile technologies make it much
easier to collect information from patients in large scale. While “big” medical data offers unprecedented
opportunities of building deep-learning artiﬁcial neural network (ANN) models to advance the research of
complex diseases such as Parkinson's disease (PD), it also presents unique challenges to patient data privacy.
This project will develop novel data masking technologies based on randomized orthogonal transformation
to enable AI-computation outsourcing and data sharing, with the following two aims: 1) Perform two
experimental studies of training ANN models with data masking in the HiperGator cloud for PD prediction
and Parkinsonism diagnosis; 2) establish the theoretical foundation on data privacy, inference accuracy, and
training performance of the ANN models used in the experimental studies.
Why do we make this supplement request? Dr. Aidong Adam Ding from Northeastern University visited
us in Summer 2022. Together we produced a manuscript that expanded our data masking method with noise
addition to achieve differential privacy (DiP) when we outsource medical data to the cloud for AI model
training. This is a signiﬁcant advance that goes beyond the originally proposed technical approaches; yet it
remains in the scope of the research plan. Therefore, we request a supplement project that utilizes our new
DiP method to transform two PD data sets for guaranteed differential privacy and make them AI-ready for
cloud-based machine learning studies. Our analysis has showed that the new DiP method could be improved
with much less noise addition, which would result in much better model accuracy. We plan to bring Dr. Ge
Han from Towson University into the team. His expertise in random forests and perturbations could help us
reduce the noise. The proposed supplement tasks for the two aims of Project R01 LM014027-01 are below.
Supplement Task to Aim 1: Produce two sharable PD data sets with differential privacy and perform a
machine learning case study on the DiP-protected data for AI-readiness evaluation. We will process our
two PD data sets with the new DiP method to ensure differential privacy. We will perform an experimental
study over the two data sets to evaluate PD-diagnosis models learned from the DiP-protected data and to
quantify the tradeoff between model accuracy and privacy protection, which helps us determine the best
conﬁguration of the privacy-protected data that we will share with the community.
Supplement Task to Aim 2: Improve the DiP Method and Enhance the Quality of AI-Ready, DiP-
Protected Data. We will reﬁne the DiP method for less noise addition, which helps improve the accuracy
of ML/AI models trained from th...

## Key facts

- **NIH application ID:** 10594084
- **Project number:** 3R01LM014027-02S1
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Shigang Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $281,290
- **Award type:** 3
- **Project period:** 2021-09-03 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10594084

## Citation

> US National Institutes of Health, RePORTER application 10594084, Supplement: SCH: Enabling Data Outsourcing and Sharing for AI-powered Parkinson's Research (3R01LM014027-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10594084. Licensed CC0.

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