Efficient Deep Learning on Multi-Source Private Data

Understand

Machine learning models benefit from large and diverse datasets.

• Using such datasets, however, often requires trusting a centralized data aggregator.
• For sensitive applications like healthcare and finance this is undesirable as it could compromise patient privacy or divulge trade secrets.
• Recent advances in secure and privacy-preserving computation, including trusted hardware enclaves and differential privacy, offer a way for mutually distrusting parties to efficiently train a machine learning model without revealing the training data.