Developing Cryptographic Tools to Keep Data Private and Secure

Machine learning and cryptography are flip sides of the same coin: one turns unstructured data into algorithms while the other hides the structure within data and algorithms. MIT-IBM researchers are exploiting these complementary traits to develop stronger cryptographic tools to keep sensitive data secure, as the health care, finance, and insurance industries, among others, handle more personal data. The researchers' goal is to build privacy protections into machine learning algorithms and make them less vulnerable to adversarial attacks.