19 citations · 36 across the 14 of their papers we have counts for
4 papers · 1 filter
SilentWood: Efficient Private Inference Over Gradient-Boosting Decision Forests
Ronny Ko, Abdelkarim Kati, Robin Geelen +8
Gradient boosting decision forests, used by XGBoost or AdaBoost, offer higher accuracy and lower training times than decision trees on large datasets. Private inference protocols f…
FastLloyd: Federated, Accurate, Secure, and Tunable -Means Clustering with Differential Privacy
Abdulrahman Diaa, Thomas Humphries, Florian Kerschbaum
We study the problem of privacy-preserving -means clustering in the horizontally federated setting. Existing federated approaches using secure computation suffer from substantia…
SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledge
Lucas Fenaux, Florian Kerschbaum
Adversarial examples are malicious inputs to machine learning models that trigger a misclassification. This type of attack has been studied for close to a decade, and we find that…
Universal Backdoor Attacks
Benjamin Schneider, Nils Lukas, Florian Kerschbaum
Web-scraped datasets are vulnerable to data poisoning, which can be used for backdooring deep image classifiers during training. Since training on large datasets is expensive, a mo…