Abstract
This work provides tight upper- and lower-bounds for the problem of mean estimation under differential privacy in the local-model, when the input is composed of n i.i.d. drawn samples from a Gaussian. Our algorithms result in a (1 − β)-confidence interval for the underlying distribution's mean µ of length O (equation presented). In addition, our algorithms leverage on binary search using local differential privacy for quantile estimation, a result which may be of separate interest. Moreover, our algorithms have a matching lower-bound, where we prove that any one-shot (each individual is presented with a single query) local differentially private algorithm must return an interval of length Ω (equation presented).
Original language | English |
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State | Published - 2020 |
Externally published | Yes |
Event | 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019 - Naha, Japan Duration: 16 Apr 2019 → 18 Apr 2019 |
Conference
Conference | 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019 |
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Country/Territory | Japan |
City | Naha |
Period | 16/04/19 → 18/04/19 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Statistics and Probability