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DARPA Contracts with Duality Technologies to Develop Privacy-Preserving Machine Learning for COVID-19 Research

DARPA Contracts with Duality Technologies to Develop Privacy-Preserving Machine Learning for COVID-19 Research

Duality Technologies, a leading provider of Privacy-Enhancing Technologies (PETs), announced today that it has contracted with the Defense Advanced Research Projects Agency (DARPA) to develop a privacy-preserving Machine Learning (ML) capability that can train models on encrypted data from multiple sources. The capabilities developed under the contract will be applied to researching genomic susceptibility to severe COVID-19 symptoms in a manner which preserves individual privacy.

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The development is based on Homomorphic Encryption (HE), a privacy-enhancing technology that allows multiple parties to analyze encrypted data and gain insights without exposing Personally Identifiable Information (PII). Duality, at the forefront of industrial research and practical implementation of HE, is leveraging its capabilities in partnership with Harvard Medical School and Two Six Labs, a research and development company in the field of data science and cybersecurity, to support Genome-Wide Association Studies (GWAS) related to COVID-19 research.

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Duality researchers have already established that applying Homomorphic Encryption to large-scale GWAS can yield results 30 times faster than alternative approaches based on Secure Multiparty Computation. GWAS assist medical research teams in understanding whether the presence of specific genomic variants in an infected person’s DNA affects their likelihood of developing severe COVID-19 symptoms. These insights may significantly improve healthcare providers’ ability to treat COVID-19 patients, yet are currently difficult to obtain as they require large sets of personal data on which predictive models can be trained. With the new machine learning capability that runs on encrypted data, researchers can pool sensitive clinical and genomic data from a variety of healthcare institutions to build predictive models while complying with global data protection and privacy regulations.

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