2.3k citations · 6.5k across the 41 of their papers we have counts for
6 papers · 1 filter
Achieving Secure and Differentially Private Computations in Multiparty Settings
Abbas Acar, Z. Berkay Celik, Hidayet Aksu +2
Sharing and working on sensitive data in distributed settings from healthcare to finance is a major challenge due to security and privacy concerns. Secure multiparty computation (S…
The Space of Transferable Adversarial Examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow +2
Adversarial examples are maliciously perturbed inputs designed to mislead machine learning (ML) models at test-time. They often transfer: the same adversarial example fools more th…
Extending Defensive Distillation
Nicolas Papernot, Patrick McDaniel
Machine learning is vulnerable to adversarial examples: inputs carefully modified to force misclassification. Designing defenses against such inputs remains largely an open problem…
Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot +3
Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale t…
Curie: Policy-based Secure Data Exchange
Z. Berkay Celik, Hidayet Aksu, Abbas Acar +3
Data sharing among partners---users, organizations, companies---is crucial for the advancement of data analytics in many domains. Sharing through secure computation and differentia…
On the (Statistical) Detection of Adversarial Examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot +2
Machine Learning (ML) models are applied in a variety of tasks such as network intrusion detection or Malware classification. Yet, these models are vulnerable to a class of malicio…