Lightning Talk: Using Data without Compromising Privacy

Deep learning and machine learning more broadly depend on large quantities of data to develop accurate predictive models. In areas such as medical research, sharing data among institutions can lead to …

Deep learning and machine learning more broadly depend on large quantities of data to develop accurate predictive models. In areas such as medical research, sharing data among institutions can lead to even greater value. However, data often includes personally identifiable information that we may not want to (or even be legally allowed to) share with others. Traditional anonymization techniques only help to some degree.In this talk, Red Hat’s Gordon Haff will share with you the active research activity taking place in academia and elsewhere into techniques such as multi-party computation and homomorphic encryption. The goal of this research is to enable broad information sharing leading to better models while preserving the anonymity of individual data points.