5 papers
Directional Influence Function: Estimating Training Data Influence in Constrained Learning
Xin Wang, R. Tyrrell Rockafellar, Xuegang +1
As constrained learning becomes increasingly common, models are trained under explicit feasibility requirements to enforce fairness, safety, robustness, regulariza- tion, and physi…
Machine Unlearning of Traffic State Estimation and Prediction
Xin Wang, R. Tyrrell Rockafellar, Xuegang +1
Data-driven traffic state estimation and prediction (TSEP) relies heavily on data sources that contain sensitive information. While the abundance of data has fueled significant bre…
Secure and Efficient -Norm Computation for Two-Party Learning Applications
Ali Arastehfard, Weiran Liu, Joshua Lee +3
Secure norm computation is becoming increasingly important in many real-world learning applications. However, existing cryptographic systems often lack a general framework for secu…
SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications
Joshua Lee, Ali Arastehfard, Weiran Liu +2
Autonomous driving and V2X technologies have developed rapidly in the past decade, leading to improved safety and efficiency in modern transportation. These systems interact with e…
Model-Targeted Data Poisoning Attacks against ITS Applications with Provable Convergence
Xin Wang, Feilong Wang, Yuan Hong +3
The growing reliance of intelligent systems on data makes the systems vulnerable to data poisoning attacks. Such attacks could compromise machine learning or deep learning models b…