1 code implementation • NeurIPS 2023 • Yingtai Xiao, Guanlin He, Danfeng Zhang, Daniel Kifer
Noisy marginals are a common form of confidentiality-protecting data release and are useful for many downstream tasks such as contingency table analysis, construction of Bayesian networks, and even synthetic data generation.
1 code implementation • 30 Nov 2022 • Yingtai Xiao, Guanhong Wang, Danfeng Zhang, Daniel Kifer
Since M* will be used no matter what, the analyst can use its output to decide whether to subsequently run M1'(thus recreating the analysis supported by M1) or M2'(recreating the analysis supported by M2), without wasting privacy loss budget.
1 code implementation • 30 Nov 2020 • Yingtai Xiao, Zeyu Ding, Yuxin Wang, Danfeng Zhang, Daniel Kifer
In practice, differentially private data releases are designed to support a variety of applications.
Databases
no code implementations • 17 Aug 2020 • Yuxin Wang, Zeyu Ding, Daniel Kifer, Danfeng Zhang
We propose CheckDP, the first automated and integrated approach for proving or disproving claims that a mechanism is differentially private.
Programming Languages D.3.1
no code implementations • 29 Apr 2019 • Zeyu Ding, Yuxin Wang, Danfeng Zhang, Daniel Kifer
We show that it can also release for free the noisy gap between the approximate maximizer and runner-up.
1 code implementation • 28 Mar 2019 • Yuxin Wang, Zeyu Ding, Guanhong Wang, Daniel Kifer, Danfeng Zhang
Sometimes, combining those two requires substantial changes to program logics: one recent paper is able to verify Report Noisy Max automatically, but it involves a complex verification system using customized program logics and verifiers.
Programming Languages D.2.4
2 code implementations • 25 May 2018 • Ding Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang, Daniel Kifer
The widespread acceptance of differential privacy has led to the publication of many sophisticated algorithms for protecting privacy.
Cryptography and Security