Reading Note of the Paper "Membership Inference Attacks on Machine Learning - A Survey"

Information of the Original Paper: Hu, H., Salcic, Z., Sun, L., Dobbie, G., Yu, P. S., & Zhang, X. (2022). Membership inference attacks on machine learning: A survey. ACM Computing Surveys (CSUR), 54(11s), 1-37.

Brief Intro

Membership Inference Attack (MIA): to infer whether a data record was used to train a target model or not.

MIAs on ML (classification, generative, etc.) models can directly lead to a privacy breach (i.e., infer privacy information from training data).

For e.g. via identifying the fact that a clinical record has been used to train a model associated with a certain disease, an attacker can infer that the owner of the clinical record has the disease with a high chance.

Key Words: privacy protection, MIA, differential privacy

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