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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.14620 |
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| _version_ | 1866913898889740288 |
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| author | Bonnéry, Daniel Bernard Jamme, Julien |
| author_facet | Bonnéry, Daniel Bernard Jamme, Julien |
| contents | The Horvitz-Thompson estimate of a total can be seen as as differentially private mechanism applied to this population total. We provide forumlae to compute the $ε$ and $δ$ parameter for this specific mecanism, coupled or not coupled with the addition of a Laplace or a Gaussian noise. This allows to determine the scale of the Laplace privacy mechanism to be added to reach a specified level of privacy, expressed in terms of $ε,δ$ differential privacy. In particular, we provide simple formulae for the special case of simple random sampling on binary data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Differential Privacy and Survey Sampling Bonnéry, Daniel Bernard Jamme, Julien Statistics Theory The Horvitz-Thompson estimate of a total can be seen as as differentially private mechanism applied to this population total. We provide forumlae to compute the $ε$ and $δ$ parameter for this specific mecanism, coupled or not coupled with the addition of a Laplace or a Gaussian noise. This allows to determine the scale of the Laplace privacy mechanism to be added to reach a specified level of privacy, expressed in terms of $ε,δ$ differential privacy. In particular, we provide simple formulae for the special case of simple random sampling on binary data. |
| title | Differential Privacy and Survey Sampling |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2506.14620 |