Between Pure and Approximate Differential Privacy
We show a new lower bound on the sample complexity of (ε,δ)-differentially private algorithms that accurately answer statistical queries on high-dimensional databases. The novelty of our bound is that it depends optimally on the parameter δ, which loosely corresponds to the probability that the algo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Labor Dynamics Institute
2017-01-01
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| Series: | The Journal of Privacy and Confidentiality |
| Subjects: | |
| Online Access: | https://journalprivacyconfidentiality.org/index.php/jpc/article/view/648 |