Membership Inference Attacks Against Machine Learning Models.¶
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership Inference Attacks Against Machine Learning Models. Proceedings of the 38th IEEE Symposium on Security and Privacy (S&P), 3-18.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Side Channel Attack
- In machine-learning security it is membership-inference attacks (a model's confidence reveals whether an input was in the training set), model-extraction attacks, and training-data extraction.
This sourceShows a model's confidence distribution leaks training-set membership — a side channel in ML — with output-perturbation defenses.
- In machine-learning security it is membership-inference attacks (a model's confidence reveals whether an input was in the training set), model-extraction attacks, and training-data extraction.
Mechanisms¶
- Membership Inference Probe
- It maps directly onto the differential-privacy guarantee, which is defined as a bound on exactly this membership advantage.
This sourceDifferential privacy is defined precisely to bound how much any single record's inclusion can change outputs, and so directly limits membership advantage.
- It maps directly onto the differential-privacy guarantee, which is defined as a bound on exactly this membership advantage.
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:fc51ecf28c6b · see in the full table