Membership Inference Attack
An attack that determines whether a specific data record was part of a model's training dataset, potentially revealing sensitive information about individuals.
In plain language
A technique where an attacker figures out if a particular person's data was used to train an AI. For example, discovering that someone's medical records were in the dataset for a health-risk AI.
Why this matters
Membership inference attacks can expose private individuals even if the model itself is not public. Any AI trained on sensitive personal data creates this risk, and Australian privacy law expects your governance to manage it actively.
Relevance
GovernanceProtecting against membership inference is a privacy and data protection obligation that requires documented risk assessment and technical controls as part of your AI governance framework.
Related terms
Putting membership inference attack into practice in your organisation?
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