7 papers
DP4SQL: Differentially Private SQL with Flexible Privacy Policies
Andrew Cascio, KinChin Tong, Daniel Kifer +2
The plausible deniability model of differential privacy for single-table datasets is well-understood. However, applying differential privacy to relational databases is much trickie…
Composition for Pufferfish Privacy
Jiamu Bai, Guanlin He, Xin Gu +2
When creating public data products out of confidential datasets, inferential/posterior-based privacy definitions, such as Pufferfish, provide compelling privacy semantics for data…
Accurate and Scalable Matrix Mechanisms via Divide and Conquer
Guanlin He, Yingtai Xiao, Jiamu Bai +4
Matrix mechanisms are often used to provide unbiased differentially private query answers when publishing statistics or creating synthetic data. Recent work has developed matrix me…
Correlating Cross-Iteration Noise for DP-SGD using Model Curvature
Xin Gu, Yingtai Xiao, Guanlin He +3
Differentially private stochastic gradient descent (DP-SGD) offers the promise of training deep learning models while mitigating many privacy risks. However, there is currently a l…
Statistics-Friendly Confidentiality Protection for Establishment Data, with Applications to the QCEW
Kaitlyn Webb, Prottay Protivash, John Durrell +3
Confidentiality for business data is an understudied area of disclosure avoidance, where legacy methods struggle to provide acceptable results. Standard formal privacy techniques f…
Fast Private Adaptive Query Answering for Large Data Domains
Miguel Fuentes, Brett Mullins, Yingtai Xiao +3
Privately releasing marginals of a tabular dataset is a foundational problem in differential privacy. However, state-of-the-art mechanisms suffer from a computational bottleneck wh…