We propose the GEARS (GEnomic Analysis Research with Security) effort with the broad goal and vision of our proposal is to enable collaboration and joint analysis of medical data, without compromising data owners? rights and complying with regulation and privacy concerns. This is achieved by introducing novel technologies from the domain of advanced cryptography that enable keeping raw data encrypted even while analyzing and computing on it. This capability is critical in order to protect against onerous data use agreements, comply with HIPAA regulations and yet speed up collaborations without the risk of re-identification. In this proposal, we focus on GWAS-like computations on genomic/phenotypic data. We plan to design platforms that enable researchers and commercial entities to efficiently share and compute over sensitive whole-genome and whole-exome data from disparate sources while it remains encrypted at all times. Data providers will both delegate to computation-hosts the ability to run computations on their encrypted data, and ?approve which results of computation can be decrypted and shared with others. The computation-host can merge encrypted data sets, from multiple parties, and enable the computations on the larger aggregated set. Our goal is to exhibit the usefulness to advance research and treatment on common and rare diseases, and in a later stage apply our methods for match-finding in the case of rare diseases that affect small percentages of the population. The case of rare diseases and rare variant studies is especially interesting as our methods make it easier? ?to? ?share? ?protected? ?access? ?to? ?varied? ?data? ?sets? ?across? ?the? ?world. The impact of our approach is to transform research and commercial abilities to collaborate on clinical and genomic data among all parties involved in the healthcare ecosystem, while preserving patients? privacy. This approach will enable to access, integrate and use larger and richer data sets quicker and more effectively, by simplifying onerous data use agreements, reducing the level of security barriers and speeding up collaboration and? ?innovation? ?to? ?better? ?address? ?both? ?common? ?and? ?rare? ?disease? ?needs. Our research will result in a prototype software implementation. This software will encrypt genomic data and will support GWAS computations in a manner that it is transparent to users that the data underneath is encrypted. We have the exact right team to succeed in our proposed research. Our team includes MIT cryptographers who have been formulating the underlying security primitives for our offering, an experienced technical team leader who has been leading homomorphic encryption implementation efforts for DARPA since their discovery, world-class medical genomics experts and experienced entrepreneurs. Our team has demonstrated that HME can be feasible for clinical and/or research application in several early prototypes. This early prototype work builds on extensive academic research into HME [4,5,8,9,11] and the open-source PALISADE? ?encryption? ?library? ?implementations? ?by? ?our? ?team? ?[10].

Public Health Relevance

We propose the GEARS (GEnomic Analysis Research with Security) effort with the broad goal and vision of our proposal is to enable collaboration and joint analysis of medical data, without compromising data owners? rights? ?and? ?complying? ?with? ?regulation? ?and? ?privacy? ?concerns. The outcomes of our research are new techniques and their prototype implementations that provide privacy-preserving data sharing and GWAS on sensitive genomic and phenotypic data joined from multiple sources. Our approach is based on the application of homomorphic encryption, which when applied at scale will transform the ability to collaborate on genomic data, derived in clinical and research setting, among all parties involved? ?in? ?the? ?healthcare? ?ecosystem,? ?while? ?preserving? ?patients?? ?privacy? ?and? ?collaborating? ?parties?? ?IP.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
1R43HG010123-01
Application #
9558454
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Sofia, Heidi J
Project Start
2018-05-01
Project End
2018-10-31
Budget Start
2018-05-01
Budget End
2018-10-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Duality Technologies, Inc.
Department
Type
DUNS #
080790286
City
Cambridge
State
MA
Country
United States
Zip Code
02140