3 citations · 4 across the 11 of their papers we have counts for
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Differential Privacy for Markov Chain State Trajectories
Alexander Benvenuti, Matthew Hale
Data-driven systems may require state trajectories of Markov chains to function because these trajectories contain information that is useful to the system, e.g., a product's credi…
Differential Privacy for Symbolic Trajectories via the Permute-and-Flip Mechanism
Alexander Benvenuti, Huaiyuan Rao, Matthew Hale
Privacy techniques have been developed for data-driven systems, but systems with non-numeric data cannot use typical noise-adding techniques. Therefore, we develop a new mechanism…
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…