Private Means and the Curious Incident of the Free Lunch
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912256342622208 |
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| author | Fitzsimons, Jack Honaker, James Shoemate, Michael Singhal, Vikrant |
| author_facet | Fitzsimons, Jack Honaker, James Shoemate, Michael Singhal, Vikrant |
| contents | We show that the most well-known and fundamental building blocks of DP implementations -- sum, mean, count (and many other linear queries) -- can be released with substantially reduced noise for the same privacy guarantee. We achieve this by projecting individual data with worst-case sensitivity $R$ onto a simplex where all data now has a constant norm $R$. In this simplex, additional ``free'' queries can be run that are already covered by the privacy-loss of the original budgeted query, and which algebraically give additional estimates of counts or sums. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10438 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Private Means and the Curious Incident of the Free Lunch Fitzsimons, Jack Honaker, James Shoemate, Michael Singhal, Vikrant Cryptography and Security We show that the most well-known and fundamental building blocks of DP implementations -- sum, mean, count (and many other linear queries) -- can be released with substantially reduced noise for the same privacy guarantee. We achieve this by projecting individual data with worst-case sensitivity $R$ onto a simplex where all data now has a constant norm $R$. In this simplex, additional ``free'' queries can be run that are already covered by the privacy-loss of the original budgeted query, and which algebraically give additional estimates of counts or sums. |
| title | Private Means and the Curious Incident of the Free Lunch |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2408.10438 |