Privacy-Preserving Machine Learning
A category of techniques and methods that enable training, deployment and operation of machine learning models while protecting the confidentiality of sensitive training data, user data and model parameters.
In plain language
A toolkit of techniques that let you build and use AI while keeping people's personal information private. Examples include federated learning (training on data without moving it) and differential privacy (adding noise to hide individuals).
Why this matters
Privacy-preserving techniques are now expected by regulators, customers and data protection advocates. Australian organisations should adopt these approaches to balance AI innovation with privacy obligations under the Privacy Act and emerging standards like the Voluntary AI Safety Standard.
Relevance
ImplementationA set of technical approaches essential for complying with privacy obligations while enabling AI innovation and building user and regulatory trust.
Related terms
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