Improved Membership Inference Attacks Against Language Classification Models
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866917725900636160 |
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| author | Shachor, Shlomit Razinkov, Natalia Goldsteen, Abigail |
| author_facet | Shachor, Shlomit Razinkov, Natalia Goldsteen, Abigail |
| contents | Artificial intelligence systems are prevalent in everyday life, with use cases in retail, manufacturing, health, and many other fields. With the rise in AI adoption, associated risks have been identified, including privacy risks to the people whose data was used to train models. Assessing the privacy risks of machine learning models is crucial to enabling knowledgeable decisions on whether to use, deploy, or share a model. A common approach to privacy risk assessment is to run one or more known attacks against the model and measure their success rate. We present a novel framework for running membership inference attacks against classification models. Our framework takes advantage of the ensemble method, generating many specialized attack models for different subsets of the data. We show that this approach achieves higher accuracy than either a single attack model or an attack model per class label, both on classical and language classification tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_07219 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improved Membership Inference Attacks Against Language Classification Models Shachor, Shlomit Razinkov, Natalia Goldsteen, Abigail Machine Learning Artificial Intelligence Cryptography and Security Artificial intelligence systems are prevalent in everyday life, with use cases in retail, manufacturing, health, and many other fields. With the rise in AI adoption, associated risks have been identified, including privacy risks to the people whose data was used to train models. Assessing the privacy risks of machine learning models is crucial to enabling knowledgeable decisions on whether to use, deploy, or share a model. A common approach to privacy risk assessment is to run one or more known attacks against the model and measure their success rate. We present a novel framework for running membership inference attacks against classification models. Our framework takes advantage of the ensemble method, generating many specialized attack models for different subsets of the data. We show that this approach achieves higher accuracy than either a single attack model or an attack model per class label, both on classical and language classification tasks. |
| title | Improved Membership Inference Attacks Against Language Classification Models |
| topic | Machine Learning Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2310.07219 |