17 citations · 31 across the 6 of their papers we have counts for
7 papers
Adaptive Model Inversion Attacks Generalize a Privacy-Robustness Tradeoff
Shailen Smith, Rasmus Torp, Adam Breuer
In this paper, we show that standard evaluations of high-resolution Model Inversion Attacks (MIAs) significantly underestimate training-data privacy leakage. State-of-the-art priva…
Reducing information dependency does not cause training data privacy. Adversarially non-robust features do
Rasmus Torp, Shailen K. Smith, Adam Breuer
In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks. We show tha…
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
Sayanton V. Dibbo, Adam Breuer, Juston Moore +1
Recent model inversion attack algorithms permit adversaries to reconstruct a neural network's private and potentially sensitive training data by repeatedly querying the network. In…
Preemptive Detection of Fake Accounts on Social Networks via Multi-Class Preferential Attachment Classifiers
Adam Breuer, Nazanin Khosravani, Michael Tingley +1
In this paper, we describe a new algorithm called Preferential Attachment k-class Classifier (PreAttacK) for detecting fake accounts in a social network. Recently, several algorith…
Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks
Adam Breuer, Roee Eilat, Udi Weinsberg
In this paper, we study the problem of early detection of fake user accounts on social networks based solely on their network connectivity with other users. Removing such accounts…
The FAST Algorithm for Submodular Maximization
Adam Breuer, Eric Balkanski, Yaron Singer
In this paper we describe a new algorithm called Fast Adaptive Sequencing Technique (FAST) for maximizing a monotone submodular function under a cardinality constraint whose ap…
Non-monotone Submodular Maximization in Exponentially Fewer Iterations
Eric Balkanski, Adam Breuer, Yaron Singer
In this paper we consider parallelization for applications whose objective can be expressed as maximizing a non-monotone submodular function under a cardinality constraint. Our mai…