# Developing novel technologies that ensure privacy and security in biomedical data science research

> **NIH NIH R35** · RUTGERS THE STATE UNIV OF NJ NEWARK · 2020 · $382,108

## Abstract

Data science holds the promise of enabling new pathways to discovery and can improve the
understanding, prevention and treatment of complex disorders such as cancer, diabetes,
substance abuse, etc., which are significantly on the rise. The promise of data science can be
fully realized only when collected data can be collaboratively shared and analyzed. However, the
widespread increases in healthcare data breaches due to inappropriate access as well as the
increasing number of novel privacy attacks restrict institutions from sharing data. Indeed, in some
cases, the results of the analysis can themselves lead to significant privacy harm. The success
of the data commons depends on ensuring the maximal access to data, subject to all of the patient
privacy requirements including those mandated by legislation, and all of the constraints of the
organization collecting the data itself. While there are existing solutions that can solve parts of the
problem, there are significant challenges in truly incorporating these into comprehensive working
solutions that are usable by the biomedical research community, and new challenges brought on
by modern techniques such as deep learning. The long-term goal of this research is to develop
technologies that can holistically enable data sharing while respecting privacy and security
considerations and to ensure that they are implemented in existing platforms that have
widespread acceptance in the research community. Towards this, the objective of this project is
to develop complementary solutions for risk inference, distributed learning, and access control
that can enable different modalities of data sharing. The problems studied are general in nature
and will evolve depending on research successes and new impediments that arise. The proposed
program of research is significant since lack of access to biomedical data can lead to
fragmentation of care, resulting in higher economic and social costs, and is a significant
impediment to biomedical research. The project will result in open-source, freely available
software tools that will be integrated into widely used data collection, cohort identification, and
distributed analytics platforms. There are several ongoing collaborations that will serve as initial
pilot customers to provide use cases, identify the requirements, evaluate results, and in general
validate the developed solutions.

## Key facts

- **NIH application ID:** 9851602
- **Project number:** 1R35GM134927-01
- **Recipient organization:** RUTGERS THE STATE UNIV OF NJ NEWARK
- **Principal Investigator:** Jaideep Vaidya
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $382,108
- **Award type:** 1
- **Project period:** 2020-01-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9851602

## Citation

> US National Institutes of Health, RePORTER application 9851602, Developing novel technologies that ensure privacy and security in biomedical data science research (1R35GM134927-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9851602. Licensed CC0.

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