InfTDA: A Simple TopDown Mechanism for Hierarchical Differentially Private Counting Queries
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916726670622720 |
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| author | Boninsegna, Fabrizio |
| author_facet | Boninsegna, Fabrizio |
| contents | This paper extends $\texttt{InfTDA}$, a mechanism proposed in (Boninsegna, Silvestri, PETS 2025) for mobility datasets with origin and destination trips, in a general setting. The algorithm presented in this paper works for any dataset of $d$ categorical features and produces a differentially private synthetic dataset that answers all hierarchical queries, a special case of marginals, each with bounded maximum absolute error. The algorithm builds upon the TopDown mechanism developed for the 2020 US Census. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InfTDA: A Simple TopDown Mechanism for Hierarchical Differentially Private Counting Queries Boninsegna, Fabrizio Data Structures and Algorithms This paper extends $\texttt{InfTDA}$, a mechanism proposed in (Boninsegna, Silvestri, PETS 2025) for mobility datasets with origin and destination trips, in a general setting. The algorithm presented in this paper works for any dataset of $d$ categorical features and produces a differentially private synthetic dataset that answers all hierarchical queries, a special case of marginals, each with bounded maximum absolute error. The algorithm builds upon the TopDown mechanism developed for the 2020 US Census. |
| title | InfTDA: A Simple TopDown Mechanism for Hierarchical Differentially Private Counting Queries |
| topic | Data Structures and Algorithms |
| url | https://arxiv.org/abs/2505.05347 |