Development of a Secure Private Neural Network Capability
- Magazine Article
Machine Learning (ML) tools like Deep Neural Networks (DNNs) have gained widespread popularity due to their ability to quickly and accurately perform discriminative tasks, such as object detection and classification. However, current implementations of this concept have several drawbacks. First, traditional DNNs require access to unprotected (unencrypted) data. Even if the data is secured and the ML tool is made compatible for use with encrypted data, the resulting operational performance is slowed to the point that it renders the approach intractable. Second, recent research has shown many DNNs are susceptible to white box (full access to the machine learning tool and operations) and black box (only access to system input and output) attacks, allowing adversaries to maliciously manipulate the ML tool's output.