| Corresponding author* |
| Data Privacy |
| Guest editors: Jin Lei, Aleksandra Slavković and Linjun Zhang (in alphabetical order) |
| |
【Foreword】Data Privacy: Overview
Jin Lei, Aleksandra Slavković and Linjun Zhang* |
|
533 |
| |
One Step to Efficient Synthetic Data
Jordan Awan* and Zhanrui Cai |
Supp |
doi:10.5705/ss.202022.0274
539 |
| |
Mechanisms for Global Differential Privacy Under Bayesian Data Synthesis
Jingchen Hu*, Matthew R. Williams and Terrance D. Savitsky |
Supp |
doi:10.5705/ss.202022.0162
563 |
| |
Inference With Combining Rules From Multiple Differentially Private Synthetic Datasets
Leila Nombo* and Anne-Sophie Charest |
Supp |
doi:10.5705/ss.202020.0299
585 |
| |
On Rate Optimal Private Regression Under Local Differential Privacy
László Györfi and Martin Kroll* |
|
doi:10.5705/ss.202022.0186
613 |
| |
Assessing Statistical Disclosure Risk for Differentially Private, Hierarchical Count Data, With Application to the 2020 U.S. Decennial Census
Zeki Kazan* and Jerome P. Reiter |
Supp |
doi:10.5705/ss.202022.0187
629 |
| |
Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy
Christian Covington*, Xi He, James Honaker and Gautam Kamath |
Supp |
doi:10.5705/ss.202022.0276
651 |
| |
Differentially Private Hypothesis Testing With the Subsampled and Aggregated Randomized Response Mechanism
Victor Peña* and Andrés Barrientos |
Supp |
doi:10.5705/ss.202022.0279
671 |
| |
Differentially Private Regularized Stochastic Convex Optimization With Heavy-Tailed Data
Haihan Xie, Matthew Pietrosanu, Yi Liu, Wei Tu, Bei Jiang and Linglong Kong* |
Supp |
doi:10.5705/ss.202022.0282
693 |
| |
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