3 citations · 8 across the 11 of their papers we have counts for
13 papers
End-to-End Differential Privacy in Training Deep Neural Network Classifiers
Huaiyuan Rao, Calvin Hawkins, Alexander Benvenuti +1
Differentially private machine learning enables model training on sensitive data while ensuring that individual data is unlikely to be recoverable from the parameters of the result…
Online Scalarization in Vector-Valued Games
Ehsan Asadollahi, Calvin Hawkins, Matthew Hale
We study repeated multi-player vector-valued games in which a player observes a payoff vector each round and evaluates outcomes through linear scalarizations of those vectors. Diff…
Differentially Private Data-Driven Markov Chain Modeling
Alexander Benvenuti, Brandon Fallin, Calvin Hawkins +4
Markov chains model a wide range of user behaviors. However, generating accurate Markov chain models requires substantial user data, and sharing these models without privacy protec…
Approximately Optimal Multi-Stream Quickest Change Detection
Joshua Kartzman, Calvin Hawkins, Matthew Hale
This paper considers the constrained sampling multi-stream quickest change detection problem, also known as the bandit quickest change detection problem. One stream contains a chan…
Generating Differentially Private Networks with a Modified Erdős-Rényi Model
Huaiyuan Rao, Calvin Hawkins, Alexander Benvenuti +1
Differential privacy has been used to privately calculate numerous network properties, but existing approaches often require the development of a new privacy mechanism for each pro…
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…
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…
Differentially Private Formation Control: Privacy and Network Co-Design
Calvin Hawkins, Matthew Hale
Privacy in multi-agent control is receiving increased attention, though often a networked system and privacy protections are designed separately, which can harm performance. Theref…
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…
Differentially Private Formation Control
Calvin Hawkins, Matthew Hale
As multi-agent systems proliferate, there is increasing demand for coordination protocols that protect agents' sensitive information while allowing them to collaborate. To help add…