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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2310.13786 |
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| _version_ | 1866909829296029696 |
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| author | Aubinais, Eric Gassiat, Elisabeth Piantanida, Pablo |
| author_facet | Aubinais, Eric Gassiat, Elisabeth Piantanida, Pablo |
| contents | Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensitive information about individuals. This article provides theoretical guarantees by exploring the fundamental statistical limitations associated with MIAs on machine learning models at large. More precisely, we first derive the statistical quantity that governs the effectiveness and success of such attacks. We then theoretically prove that in a non-linear regression setting with overfitting learning procedures, attacks may have a high probability of success. Finally, we investigate several situations for which we provide bounds on this quantity of interest. Interestingly, our findings indicate that discretizing the data might enhance the learning procedure's security. Specifically, it is demonstrated to be limited by a constant, which quantifies the diversity of the underlying data distribution. We illustrate those results through simple simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_13786 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fundamental Limits of Membership Inference Attacks on Machine Learning Models Aubinais, Eric Gassiat, Elisabeth Piantanida, Pablo Machine Learning Artificial Intelligence Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensitive information about individuals. This article provides theoretical guarantees by exploring the fundamental statistical limitations associated with MIAs on machine learning models at large. More precisely, we first derive the statistical quantity that governs the effectiveness and success of such attacks. We then theoretically prove that in a non-linear regression setting with overfitting learning procedures, attacks may have a high probability of success. Finally, we investigate several situations for which we provide bounds on this quantity of interest. Interestingly, our findings indicate that discretizing the data might enhance the learning procedure's security. Specifically, it is demonstrated to be limited by a constant, which quantifies the diversity of the underlying data distribution. We illustrate those results through simple simulations. |
| title | Fundamental Limits of Membership Inference Attacks on Machine Learning Models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2310.13786 |