120 citations · 197 across the 4 of their papers we have counts for
4 papers
Dataset Inference: Ownership Resolution in Machine Learning
Pratyush Maini, Mohammad Yaghini, Nicolas Papernot
With increasingly more data and computation involved in their training, machine learning models constitute valuable intellectual property. This has spurred interest in model steali…
Entangled Watermarks as a Defense against Model Extraction
Hengrui Jia, Christopher A. Choquette-Choo, Varun Chandrasekaran +1
Machine learning involves expensive data collection and training procedures. Model owners may be concerned that valuable intellectual property can be leaked if adversaries mount mo…
A General Approach to Adding Differential Privacy to Iterative Training Procedures
H. Brendan McMahan, Galen Andrew, Ulfar Erlingsson +4
In this work we address the practical challenges of training machine learning models on privacy-sensitive datasets by introducing a modular approach that minimizes changes to train…
A Marauder's Map of Security and Privacy in Machine Learning
Nicolas Papernot
There is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nat…