# Decentralized differentially-private methods for dynamic data release and analysis

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $562,025

## Abstract

PROJECT SUMMARY
Data sharing and information exchange are playing critical roles in biomedical data
science to improve quality of care, accelerate discovery, and promote meaningful
secondary use of clinical data. But privacy is a big concern to the public. Existing
distributed data analysis methods do not address the security and privacy issues in
exchanging intermediary statistics and they cannot handle dynamic database updates
very well. This project aims at designing and implementing differentially-private
decentralized methods for dynamic data dissemination and analysis. We plan to use
genomic and clinical data from both public domain and local institutions (UCSD and
Emory) to carefully evaluate the feasibility and efficiency of our proposed new methods.

## Key facts

- **NIH application ID:** 9878892
- **Project number:** 5R01GM118609-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Xiaoqian Jiang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $562,025
- **Award type:** 5
- **Project period:** 2017-01-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9878892

## Citation

> US National Institutes of Health, RePORTER application 9878892, Decentralized differentially-private methods for dynamic data release and analysis (5R01GM118609-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9878892. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*