Banco Bradesco, S.A., a prominent Brazilian financial institution, has for the past year been working with IBM Research to apply a technique called homomorphic encryption to banking data. The pilot showed it was possible to apply machine learning algorithms to encrypted data without decrypting it, creating a new level of privacy that could be applied to other industries.
Machine learning is often used in banking and finance to predict scenarios like transaction fraud or investment outcomes. This typically involves vast stores of data, much of which are sensitive but must be decrypted before processing, exposing sensitive data to exfiltration and leaks.
The idea behind homomorphic encryption (HE), now emerging in real-life applications like this one, is to keep data encrypted while it's being processed. This type of cryptography was first proposed in the 1970s; it wasn't until 2009 that IBM scientist Craig Gentry created the first fully homomorphic encryption system. HE is based on the mathematics of lattices and, researchers say, protects the confidentiality of data from complex attacks – even by quantum computers.
"In the past, we've used encryption for transmitting data," says Flavio Bergamaschi, IBM researcher and lead author of this project. When you shop online and enter your credit card number, it's encrypted to transfer but must be decrypted to do anything with it. The number is encrypted when stored on a disk, but it must be decrypted to act on it.
Bergamaschi says HE protects information from what he calls the "honest but curious" threat model. An entity performing computation may be legitimate but at the same time curious about your information: When you ask a cloud service how long it takes to g ..
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