Local, Private, Efficient Protocols for Succinct Histograms
Author:
Affiliation:
1. The Pennsylvania State University, University Park, PA, USA
Funder
National Science Foundation
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/2746539.2746632
Reference21 articles.
1. A Framework for High-Accuracy Privacy-Preserving Mining
2. The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy
3. Local Privacy and Statistical Minimax Rates
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