3 citations · 12 across the 9 of their papers we have counts for
9 papers
Scalable Distributed Reproduction Numbers of Network Epidemics with Differential Privacy
Bo Chen, Baike She, Calvin Hawkins +2
Reproduction numbers are widely used for the estimation and prediction of epidemic spreading processes over networks. However, conventional reproduction numbers of an overall netwo…
Differentially Private Computation of Basic Reproduction Numbers in Networked Epidemic Models
Bo Chen, Baike She, Calvin Hawkins +4
The basic reproduction number of a networked epidemic model, denoted , can be computed from a network's topology to quantify epidemic spread. However, disclosure of risk…
Differentially Private Reward Functions in Policy Synthesis for Markov Decision Processes
Alexander Benvenuti, Calvin Hawkins, Brandon Fallin +4
Markov decision processes often seek to maximize a reward function, but onlookers may infer reward functions by observing the states and actions of such systems, revealing sensitiv…
Differential Privacy in Cooperative Multiagent Planning
Bo Chen, Calvin Hawkins, Mustafa O. Karabag +3
Privacy-aware multiagent systems must protect agents' sensitive data while simultaneously ensuring that agents accomplish their shared objectives. Towards this goal, we propose a f…
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…
Differential Privacy for Symbolic Systems with Application to Markov Chains
Bo Chen, Kevin Leahy, Austin Jones +1
Data-driven systems are gathering increasing amounts of data from users, and sensitive user data requires privacy protections. In some cases, the data gathered is non-numerical or…
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…