Homomorphic Encryption: Safeguarding Sensitive Data for Smarter AI

Thanks to advances in technology, we might soon be able to use sensitive data for machine learning without customers having to reveal their confidential information. Machine learning systems need access to huge volumes of data in order to learn thoroughly. But how secure is the data used to train the machine, especially if it's confidential information? Can it be traced or even hacked? Should we even use sensitive data for machine learning at all? SAP reported on the launch of SAP's guiding principles on artificial intelligence (AI) in 2018. One example of how SAP lives by these principles itself is homomorphic encryption.