7 citations · 10 across the 4 of their papers we have counts for
4 papers
ClarAVy: A Tool for Scalable and Accurate Malware Family Labeling
Robert J. Joyce, Derek Everett, Maya Fuchs +2
Determining the family to which a malicious file belongs is an essential component of cyberattack investigation, attribution, and remediation. Performing this task manually is time…
Assemblage: Automatic Binary Dataset Construction for Machine Learning
Chang Liu, Rebecca Saul, Yihao Sun +5
Binary code is pervasive, and binary analysis is a key task in reverse engineering, malware classification, and vulnerability discovery. Unfortunately, while there exist large corp…
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile
Tyler LeBlond, Joseph Munoz, Fred Lu +4
Differential privacy (DP) is the prevailing technique for protecting user data in machine learning models. However, deficits to this framework include a lack of clarity for selecti…
A General Framework for Auditing Differentially Private Machine Learning
Fred Lu, Joseph Munoz, Maya Fuchs +5
We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. While previous works have taken steps toward…