The Discrete Gaussian for Differential Privacy
A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution presents several practical challenges. First and foremost, finite computers cannot exactly represent samples f...
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| Huvudupphov: | , , |
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| Materialtyp: | Artigo |
| Språk: | Inglês |
| Utgiven: |
Labor Dynamics Institute
2022-07-01
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| Serie: | The Journal of Privacy and Confidentiality |
| Ämnen: | |
| Länkar: | http://www.journalprivacyconfidentiality.org/index.php/jpc/article/view/784 |
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