Recently, Facebook AI open-sourced a new high-speed library for training PyTorch models with differential privacy (DP) known as Opacus. The library is claimed to be more scalable than existing state-of-the-art methods.

According to the developers at the social media giant, differential privacy is a mathematically rigorous framework for quantifying the anonymisation of sensitive data. With the growing interest in the machine learning (ML) community, this framework is often used in analytics and computations.

Differential privacy constitutes a strong standard for privacy guarantees for algorithms on aggregate databases. It is usually defined in terms of the application-specific concept of adjacent databases. The framework has several properties that make it particularly useful in applications, such as group privacy, robustness to auxiliary information, among others.

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