Privacy Preservation in Transcriptomic Data Analysis

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.