32 citations · 37 across the 4 of their papers we have counts for
4 papers
When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes
Canyu Chen, Yueqing Liang, Xiongxiao Xu +5
Machine learning models have demonstrated promising performance in many areas. However, the concerns that they can be biased against specific demographic groups hinder their adopti…
DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning
Meisam Mohammady, Han Wang, Lingyu Wang +6
Outsourcing anomaly detection to third-parties can allow data owners to overcome resource constraints (e.g., in lightweight IoT devices), facilitate collaborative analysis (e.g., u…
RDP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions
Meisam Mohammady, Shangyu Xie, Yuan Hong +4
Differential privacy (DP) has emerged as a de facto standard privacy notion for a wide range of applications. Since the meaning of data utility in different applications may vastly…
Preserving Both Privacy and Utility in Network Trace Anonymization
Meisam Mohammady, Lingyu Wang, Yuan Hong +3
As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctan…