2 citations · 4 across the 6 of their papers we have counts for
8 papers
Unifying Privacy Accounting: Information Equivalence and Information Loss
Buxin Su, Qiaoshi Yang, Yiding Su +1
Differential privacy (DP) admits several notions, but the choice among them may affect both privacy analysis and utility. In this paper, we consider four mainstream curve-based pri…
A Sieve-Accelerated Quadrature Method for Exact Privacy Accounting in the 2020 U.S. Decennial Census
Buxin Su, Weijie Su, Chendi Wang
In 2020, the U.S. Census Bureau adopted differential privacy for the Decennial Census by injecting integer-valued Gaussian noise into published census tabulations. Exactly evaluati…
Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via -Differential Privacy
Xiang Li, Buxin Su, Chendi Wang +2
Differentially private (DP) decentralized Federated Learning (FL) allows local users to collaborate without sharing their data with a central server. However, accurately quantifyin…
The 2020 US Decennial Census is more private than you (might) think
Buxin Su, Weijie J. Su, Chendi Wang
The U.S. Decennial Census serves as the foundation for many high-profile policy decision-making processes, including federal funding allocation and redistricting. In 2020, the Cens…
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning
Chendi Wang, Yuqing Zhu, Weijie J. Su +1
A recent study by De et al. (2022) has reported that large-scale representation learning through pre-training on a public dataset significantly enhances differentially private (DP)…
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via -Differential Privacy
Chendi Wang, Buxin Su, Jiayuan Ye +2
Differentially private (DP) machine learning algorithms incur many sources of randomness, such as random initialization, random batch subsampling, and shuffling. However, such rand…
Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms
Ximing Li, Chendi Wang, Guang Cheng
Marginal-based methods achieve promising performance in the synthetic data competition hosted by the National Institute of Standards and Technology (NIST). To deal with high-dimens…
IPProtect: protecting the intellectual property of visual datasets during data valuation
Gursimran Singh, Chendi Wang, Ahnaf Tazwar +2
Data trading is essential to accelerate the development of data-driven machine learning pipelines. The central problem in data trading is to estimate the utility of a seller's data…