We provide an end-to-end Renyi DP based-framework for differentially private top-π selection. Unlike previous approaches, which require a data-independent choice on π, we propose to privately release a data-dependent choice of π such that the gap between π-th and the (π+1)st βqualityβ is large. This is achieved by an extension of the Report-Noisy-Max algorithm with a more concentrated Gaussian noise. Not only does this eliminates one hyperparameter, the adaptive choice of π also certifies the stability of the top-π indices in the unordered set so we can release them using a combination of the propose-test-release (PTR) framework and the Distance-to-Stability mechanism. We show that our construction improves the privacy-utility trade-offs compared to the previous top-π selection algorithms theoretically and empirically. Additionally, we apply our algorithm to βPrivate Aggregation of Teacher Ensembles (PATE)β in multi-label classification tasks with a large number of labels and show that it leads to significant performance gains.
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A calcium signalling network activates vacuolar K+ remobilization to enable plant adaptation to low-K environments
- Award ID(s):
- 1714795
- PAR ID:
- 10165666
- Date Published:
- Journal Name:
- Nature Plants
- Volume:
- 6
- Issue:
- 4
- ISSN:
- 2055-0278
- Page Range / eLocation ID:
- 384 to 393
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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