In the world of data analytics, privacy restrictions can get in the way of analysis. It’s a classic two-edged sword, and finding a flexible but secure solution to assure both privacy and deep data access has been difficult to achieve.
In the case of medical records, security protocols to assure compliance can essentially lock down analysis, says Noah Johnson, a UC Berkeley graduate student in electrical engineering and computer science with both a research background and frontline experience in designing practical security tools.
“If an organization can’t distinguish between what is allowable and what is overstepping security boundaries, the only safe solution is to block all access to the data,” he says.
Supported by the Signatures Innovation Fellows program, Johnson and Dawn Song, a professor in EECS and a leader in the cybersecurity field, are taking an entirely new approach to enable organizations to follow tight data security and privacy policies while enabling flexible data analysis, as well as machine learning for analysts.
Working with Uber, they tested their system using a dataset of 8 million queries written by the company’s data analysts. The system is currently being integrated into Uber’s internal data analytics platform.
With help from the Signatures Innovation Fellows program, they are advancing the system to provide the same level of security and flexibility for a broad range of data analysis and machine learning, whether needed in basic and medical research or business analytics.