7 citations · 17 across the 5 of their papers we have counts for
5 papers
PEPSI: Practically Efficient Private Set Intersection in the Unbalanced Setting
Rasoul Akhavan Mahdavi, Nils Lukas, Faezeh Ebrahimianghazani +7
Two parties with private data sets can find shared elements using a Private Set Intersection (PSI) protocol without revealing any information beyond the intersection. Circuit PSI p…
Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
Abdulrahman Diaa, Lucas Fenaux, Thomas Humphries +9
Machine Learning as a Service (MLaaS) is an increasingly popular design where a company with abundant computing resources trains a deep neural network and offers query access for t…
Towards Robust Dataset Learning
Yihan Wu, Xinda Li, Florian Kerschbaum +2
Adversarial training has been actively studied in recent computer vision research to improve the robustness of models. However, due to the huge computational cost of generating adv…
Recovery from Non-Decomposable Distance Oracles
Zhuangfei Hu, Xinda Li, David P. Woodruff +2
A line of work has looked at the problem of recovering an input from distance queries. In this setting, there is an unknown sequence , and one chooses a set…
SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)
Nils Lukas, Edward Jiang, Xinda Li +1
Deep Neural Network (DNN) watermarking is a method for provenance verification of DNN models. Watermarking should be robust against watermark removal attacks that derive a surrogat…