# Privacy Preservation in Transcriptomic Data Analysis

> **NIH NIH R56** · NEW YORK GENOME CENTER · 2024 · $855,711

## Abstract

PROJECT SUMMARY
Understanding the mechanism behind cellular activities requires large-scale mining of genetic and transcriptomic
observations from large amounts of human data. Widespread and easy access to such data is imperative to
make biological connections between genes and diseases. However, there is a direct conflict between protecting
the privacy of patients and research participants and broad sharing of genetic and transcriptomic data for
biomedical advances. In order to address these privacy concerns during transcriptomic analysis, we propose to
take advantage of cryptographic approaches that enable direct computations on encrypted data without revealing
the sensitive information in them. We will create an evolving and modular tool suite to preserve privacy; this suite
will have the ability to be adopted to new data modalities and analysis needs as they arise. In particular, we
propose to develop a series of tools that can quantify the bulk transcript and single-cell gene expression and
perform eQTL mapping on the encrypted genotypes in a shared server and cloud setting. The proposed tools
will help prevent future catastrophic privacy leaks, which may result in a loss of access to all medically actionable
data. Our long-term goal is to democratize data access for all researchers and create trust between patients and
researchers, thus increasing participation in studies.

## Key facts

- **NIH application ID:** 11174987
- **Project number:** 1R56HG013319-01A1
- **Recipient organization:** NEW YORK GENOME CENTER
- **Principal Investigator:** Gamze Gursoy
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $855,711
- **Award type:** 1
- **Project period:** 2024-09-23 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11174987

## Citation

> US National Institutes of Health, RePORTER application 11174987, Privacy Preservation in Transcriptomic Data Analysis (1R56HG013319-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11174987. Licensed CC0.

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