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Membership Inference is an attack that determines whether a specific data record was part of a model's training dataset by analysing the model's output behaviour for that record.
Membership Inference, an attack that determines whether a specific data record was part of a model's training dataset by analysing the model's output behaviour for that record.
Membership inference is a privacy attack with significant regulatory implications. If an attacker can determine that a specific individual's data was used to train a model, this may constitute a privacy breach, especially where the individual did not consent to their data being used for AI training. GDPR Article 17 (right to erasure) creates obligations that membership inference makes hard to satisfy: deleting a record from a database may not remove its influence from a trained model. Machine unlearning research addresses this problem.
Source: Shokri et al. (2017); GDPR, Article 17
Membership inference is a privacy attack with significant regulatory implications. If an attacker can determine that a specific individual's data was used to train a model, this may constitute a privacy breach, especially where the individual did not consent to their data being used for AI training. GDPR Article 17 (right to erasure) creates obligations that membership inference makes hard to satisfy: deleting a record from a database may not remove its influence from a trained model. Machine unlearning research addresses this problem.
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