55 citations · 194 across the 35 of their papers we have counts for
9 papers · 1 filter
Privacy-Preserving Collaborative Prediction using Random Forests
Irene Giacomelli, Somesh Jha, Ross Kleiman +2
We study the problem of privacy-preserving machine learning (PPML) for ensemble methods, focusing our effort on random forests. In collaborative analysis, PPML attempts to solve th…
Exploring Connections Between Active Learning and Model Extraction
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli +2
Machine learning is being increasingly used by individuals, research institutions, and corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud…
Concise Explanations of Neural Networks using Adversarial Training
Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury +2
We show new connections between adversarial learning and explainability for deep neural networks (DNNs). One form of explanation of the output of a neural network model in terms of…
Explainable Black-Box Attacks Against Model-based Authentication
Washington Garcia, Joseph I. Choi, Suman K. Adari +2
Establishing unique identities for both humans and end systems has been an active research problem in the security community, giving rise to innovative machine learning-based authe…
Neural-Augmented Static Analysis of Android Communication
Jinman Zhao, Aws Albarghouthi, Vaibhav Rastogi +2
We address the problem of discovering communication links between applications in the popular Android mobile operating system, an important problem for security and privacy in Andr…
Adversarial Binaries for Authorship Identification
Xiaozhu Meng, Barton P. Miller, Somesh Jha
Binary code authorship identification determines authors of a binary program. Existing techniques have used supervised machine learning for this task. In this paper, we look this p…