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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | https://arxiv.org/abs/2401.18024 |
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| _version_ | 1866917628456468480 |
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| author | Maddi, Aadyaa Routray, Swadhin Goldberg, Alexander Fanti, Giulia |
| author_facet | Maddi, Aadyaa Routray, Swadhin Goldberg, Alexander Fanti, Giulia |
| contents | Differential privacy (DP) is increasingly used to protect the release of hierarchical, tabular population data, such as census data. A common approach for implementing DP in this setting is to release noisy responses to a predefined set of queries. For example, this is the approach of the TopDown algorithm used by the US Census Bureau. Such methods have an important shortcoming: they cannot answer queries for which they were not optimized. An appealing alternative is to generate DP synthetic data, which is drawn from some generating distribution. Like the TopDown method, synthetic data can also be optimized to answer specific queries, while also allowing the data user to later submit arbitrary queries over the synthetic population data. To our knowledge, there has not been a head-to-head empirical comparison of these approaches. This study conducts such a comparison between the TopDown algorithm and private synthetic data generation to determine how accuracy is affected by query complexity, in-distribution vs. out-of-distribution queries, and privacy guarantees. Our results show that for in-distribution queries, the TopDown algorithm achieves significantly better privacy-fidelity tradeoffs than any of the synthetic data methods we evaluated; for instance, in our experiments, TopDown achieved at least $20\times$ lower error on counting queries than the leading synthetic data method at the same privacy budget. Our findings suggest guidelines for practitioners and the synthetic data research community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_18024 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarking Private Population Data Release Mechanisms: Synthetic Data vs. TopDown Maddi, Aadyaa Routray, Swadhin Goldberg, Alexander Fanti, Giulia Cryptography and Security Differential privacy (DP) is increasingly used to protect the release of hierarchical, tabular population data, such as census data. A common approach for implementing DP in this setting is to release noisy responses to a predefined set of queries. For example, this is the approach of the TopDown algorithm used by the US Census Bureau. Such methods have an important shortcoming: they cannot answer queries for which they were not optimized. An appealing alternative is to generate DP synthetic data, which is drawn from some generating distribution. Like the TopDown method, synthetic data can also be optimized to answer specific queries, while also allowing the data user to later submit arbitrary queries over the synthetic population data. To our knowledge, there has not been a head-to-head empirical comparison of these approaches. This study conducts such a comparison between the TopDown algorithm and private synthetic data generation to determine how accuracy is affected by query complexity, in-distribution vs. out-of-distribution queries, and privacy guarantees. Our results show that for in-distribution queries, the TopDown algorithm achieves significantly better privacy-fidelity tradeoffs than any of the synthetic data methods we evaluated; for instance, in our experiments, TopDown achieved at least $20\times$ lower error on counting queries than the leading synthetic data method at the same privacy budget. Our findings suggest guidelines for practitioners and the synthetic data research community. |
| title | Benchmarking Private Population Data Release Mechanisms: Synthetic Data vs. TopDown |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2401.18024 |