37 citations · 65 across the 4 of their papers we have counts for
5 papers
Measuring Database Unfairness via Dependency Quantification Under Differential Privacy
Mariia Vologdin, Yuchao Tao, Amir Gilad
Differential privacy (DP) has become the de facto standard for protecting sensitive data, providing strong guarantees that published statistics or models reveal limited information…
DPXPlain: Privately Explaining Aggregate Query Answers
Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala +1
Differential privacy (DP) is the state-of-the-art and rigorous notion of privacy for answering aggregate database queries while preserving the privacy of sensitive information in t…
Benchmarking Differentially Private Synthetic Data Generation Algorithms
Yuchao Tao, Ryan McKenna, Michael Hay +2
This work presents a systematic benchmark of differentially private synthetic data generation algorithms that can generate tabular data. Utility of the synthetic data is evaluated…
Prior-Aware Distribution Estimation for Differential Privacy
Yuchao Tao, Johes Bater, Ashwin Machanavajjhala
Joint distribution estimation of a dataset under differential privacy is a fundamental problem for many privacy-focused applications, such as query answering, machine learning task…
Computing Local Sensitivities of Counting Queries with Joins
Yuchao Tao, Xi He, Ashwin Machanavajjhala +1
Local sensitivity of a query Q given a database instance D, i.e. how much the output Q(D) changes when a tuple is added to D or deleted from D, has many applications including quer…