Preserving privacy while sharing data

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 a degree. In this talk, Red Hat's Gordon Haff will share with you the active activity taking place in academia, open source communities, and elsewhere into techniques such as differential privacy and secure multi-party computation. The goal of this research and ongoing work is to help individuals and organizations work collaboratively while preserving the anonymity of individual data points.