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MIT, Stanford Project Protects Security of Genomic Data for Open Research

In a paper appearing in the journal Nature Biotechnology, researchers from MIT and Stanford University have described a new system they've developed for protecting the privacy of people who contribute their genomic data to large-scale biomedical studies. These studies are intended to uncover links among genetic variations in identifying the causes for diseases.

As the researchers explained, most sequenced genomes are currently kept in "strict access-controlled repositories." Giving free access to the data through "association studies" could speed up the research. Yet, people concerned about the data privacy of their genetic make-ups may refrain from contributing their genomes to scientific studies. For example, one expert claimed to be able to analyze raw genomic data to determine the shape of faces; and researchers have shown how to triangulate genomic information with other data to elicit the identity of somebody.

The protocol, developed by MIT's Hyunghoon Cho and Bonnie Berger and Stanford's David Wu, is intended to help make currently restricted data available to the scientific community, potentially enabling secure genome crowdsourcing while still making sure individuals can contribute their genomes to a study without compromising their privacy.

The heart of the technique is to distribute sensitive data among multiple servers. As an MIT article on the topic explained, to store the number x, the system might send a random number, r, to one server, and x-r to another. Neither server would be able to calculate x on its own. But together, they could "still perform useful operations." If a cybercriminal wanted to figure out what x was, he or she would need to break into both servers — or as many servers as were involved. As servers are added to the setup, the cryptography approach becomes more complicated.

Association studies involve a massive table — or matrix — that maps the genomes in the database against the locations of genetic variations. These variations typically number about a million, requiring a million-by-million matrix, making security a complicated affair and the research effort time-consuming.

But Cho, Berger and Wu have developed techniques to simplify the security calculations and speed up the processing of their system. Based on those techniques, the system accurately reproduced three published genome-wide association studies involving up to 23,000 individual genomes. The approach could feasibly scale to a million individuals, they predict.

"As biomedical researchers, we're frustrated by the lack of data and by the access-controlled repositories," said Berger, a professor of math. "We anticipate a future with a landscape of massively distributed genomic data, where private individuals take ownership of their own personal genomes, and institutes as well as hospitals build their own private genomic databases. Our work provides a roadmap for pooling together this vast amount of genomic data to enable scientific progress."

The paper is available behind a registration wall at Nature Biotechnology.

About the Author

Dian Schaffhauser is a former senior contributing editor for 1105 Media's education publications THE Journal, Campus Technology and Spaces4Learning.

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