6 citations · 11 across the 5 of their papers we have counts for
5 papers
Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation
Sajani Vithana, Sangwon Jung, Haoyang Hu +3
Differential privacy (DP) imposes fundamental trade-offs between privacy and statistical fidelity in synthetic data generation. While access to public data has been shown to improv…
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation
Sajani Vithana, Viveck R. Cadambe, Flavio P. Calmon +1
Differentially private distributed mean estimation (DP-DME) is a fundamental building block in privacy-preserving federated learning, where a central server estimates the mean of $…
Differentially Private Secure Multiplication: Hiding Information in the Rubble of Noise
Viveck R. Cadambe, Ateet Devulapalli, Haewon Jeong +1
We consider the problem of private distributed multi-party multiplication. It is well-established that Shamir secret-sharing coding strategies can enable perfect information-theore…
Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
Wael Alghamdi, Hsiang Hsu, Haewon Jeong +4
We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and pote…
Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values
Haewon Jeong, Hao Wang, Flavio P. Calmon
We investigate the fairness concerns of training a machine learning model using data with missing values. Even though there are a number of fairness intervention methods in the lit…