388 citations · 1.5k across the 94 of their papers we have counts for
8 papers · 1 filter
Detection and Localization of Load Redistribution Attacks on Large Scale Systems
Andrea Pinceti, Lalitha Sankar, Oliver Kosut
A nearest neighbor-based detection scheme against load redistribution attacks is presented. The detector is designed to scale from small to very large systems while guaranteeing co…
Theoretical Guarantees for Model Auditing with Finite Adversaries
Mario Diaz, Peter Kairouz, Jiachun Liao +1
Privacy concerns have led to the development of privacy-preserving approaches for learning models from sensitive data. Yet, in practice, even models learned with privacy guarantees…
Generating Fair Universal Representations using Adversarial Models
Peter Kairouz, Jiachun Liao, Chong Huang +3
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our f…
A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization
Tyler Sypherd, Mario Diaz, John Kevin Cava +3
We introduce a tunable loss function called -loss, parameterized by , which interpolates between the exponential loss (), the log-loss (), and the…
Can Predictive Filters Detect Gradually Ramping False Data Injection Attacks Against PMUs?
Zhigang Chu, Andrea Pinceti, Reetam Sen Biswas +3
Intelligently designed false data injection (FDI) attacks have been shown to be able to bypass the -test based bad data detector (BDD), resulting in physical consequences (suc…
Vulnerability Assessment of N-1 Reliable Power Systems to False Data Injection Attacks
Zhigang Chu, Jiazi Zhang, Oliver Kosut +1
This paper studies the vulnerability of large-scale power systems to false data injection (FDI) attacks through their physical consequences. Prior work has shown that an attacker-d…