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Membership Inference Attacks on ML Models
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LipariSC_MIAipynb
Implementation of Salem's adversaries against models trained on toy data.
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LipariSC_MIA-Saferipynb
Implementation of the Salem adversaries against safer ML models trained on...
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Item URL
https://data.d4science.org/ctlg/ResourceCatalogue/membership_inference_attacks_on_ml_models |
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Additional Info
Field | Value |
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Detailed description | These experiments follow the paper by Salem et al. "ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models". The experiment showcases membership inference attacks against machine learning models using toy data.In particular, we generate synthetic datasets of points using scikit-learn's make_classification function and split them into target, test, and independent sets. Then, we train a target model and run three different attacks as described in the paper. We provide different performance metrics attacks.The second experiment makes some changes in how the toy data is generated in order to obtain less vulnerable models. In particular, we aim for less overfit models so that MIA risk is reduced. |
Ethical issues | No ethical issues were identified. All data used is synthetically generated using scikit-learn's make_classification function, which outputs combinations of Gaussian distributed random points and assigns them a class.make_classification: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html |
Group | Others |
Involved Institutions | Universitat Rovira i Virgili |
Involved People | Blanco-Justicia, Alberto, alberto.blanco@urv.cat, orcid.org/0000-0002-1108-8082 |
Involved People | Domingo-Ferrer, Josep, josep.domingo@urv.cat, orcid.org/0000-0001-7213-4962 |
State | Complete |
Thematic Cluster | Privacy Enhancing Technology [PET] |
ThematicCluster | Social Data |
system:type | Experiment |
Management Info
Field | Value |
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Author | Blanco Justicia Alberto |
Maintainer | Blanco Justicia Alberto |
Version | 1 |
Last Updated | 30 May 2023, 08:56 (CEST) |
Created | 29 May 2023, 20:29 (CEST) |