7 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…
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…
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…
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…
Generating Differentially Private Networks with a Modified ErdÅs-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…