2 citations · 3 across the 4 of their papers we have counts for
6 papers
SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
Yan Zhou, Bradley Malin, Murat Kantarcioglu
Privacy-preserving data publication, including synthetic data sharing, often experiences trade-offs between privacy and utility. Synthetic data is generally more effective than dat…
Bayes-Nash Generative Privacy Against Membership Inference Attacks
Tao Zhang, Rajagopal Venkatesaramani, Rajat K. De +2
Membership inference attacks (MIAs) pose significant privacy risks by determining whether individual data is in a dataset. While differential privacy (DP) mitigates these risks, it…
Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets
Victor Borza, Andrew Estornell, Ellen Wright Clayton +4
Large participatory biomedical studies, studies that recruit individuals to join a dataset, are gaining popularity and investment, especially for analysis by modern AI methods. Bec…
Differential Confounding Privacy and Inverse Composition
Tao Zhang, Bradley A. Malin, Netanel Raviv +1
Differential privacy (DP) has become the gold standard for privacy-preserving data analysis, but its applicability can be limited in scenarios involving complex dependencies betwee…
Dataset Representativeness and Downstream Task Fairness
Victor Borza, Andrew Estornell, Chien-Ju Ho +2
Our society collects data on people for a wide range of applications, from building a census for policy evaluation to running meaningful clinical trials. To collect data, we typica…
A Game-Theoretic Approach to Privacy-Utility Tradeoff in Sharing Genomic Summary Statistics
Tao Zhang, Rajagopal Venkatesaramani, Rajat K. De +2
The advent of online genomic data-sharing services has sought to enhance the accessibility of large genomic datasets by allowing queries about genetic variants, such as summary sta…