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Description
Nowadays we produce a huge amount of digital data just with our daily experience. This information could be precious, particularly when it comes to the healthcare industry, and yet its potential lies untouched because of the sensitivity of the information and the privacy issues related to it. That’s why we developed Maia: a solution fueled by Random Power to collect, access and analyze aggregated health datasets without privacy issues. It relies on the concept of differential privacy to conceal personal information while preserving the statistical trends of the dataset by adding a calibrated random noise to the pool of data. Our approach could enable the creation of health datasets for statistical and predictive studies, opening up a future where the secondary use of medical data would enable faster diagnosis, better therapy and more effective policies.
