As any data scientist will tell you, datasets are the lifeblood of artificial intelligence (AI). That poses an inherent challenge for industries dealing in personally identifiable information (e.g., health care), but fortunately, encouraging progress has been made toward an anonymized, encrypted approach to model training.

Today at the NeurIPS 2018 conference in Montreal, Canada, Intel announced that it has open-sourced , a tool that allows AI systems to operate on sensitive data. It’s a backend for , Intel’s neural network compiler, and based on the Simple Encrypted Arithmetic Library (), an encryption library Microsoft Research also released in open source this

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