1 citations · 1 across the 3 of their papers we have counts for
4 papers
The Bounded Gaussian Mechanism for Differential Privacy
Bo Chen, Matthew Hale
The Gaussian mechanism is one differential privacy mechanism commonly used to protect numerical data. However, it may be ill-suited to some applications because it has unbounded su…
Node and Edge Differential Privacy for Graph Laplacian Spectra: Mechanisms and Scaling Laws
Calvin Hawkins, Bo Chen, Kasra Yazdani +1
This paper develops a framework for privatizing the spectrum of the graph Laplacian of an undirected graph using differential privacy. We consider two privacy formulations. The fir…
Edge Differential Privacy for Algebraic Connectivity of Graphs
Bo Chen, Calvin Hawkins, Kasra Yazdani +1
Graphs are the dominant formalism for modeling multi-agent systems. The algebraic connectivity of a graph is particularly important because it provides the convergence rates of con…
Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners
Parham Gohari, Bo Chen, Bo Wu +2
Kickstarting deep reinforcement learning algorithms facilitate a teacher-student relationship among the agents and allow for a well-performing teacher to share demonstrations with…