1 citations · 1 across the 4 of their papers we have counts for
7 papers
Deception in Linear-Quadratic Control
Yerin Kim, Haosheng Zhou, Alexander Benvenuti +2
Systems operating in adversarial environments may inadvertently leak sensitive information to adversaries. To address this challenge, we revisit the linear-quadratic control framew…
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…
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…
Differentially Private Linear Programming: Reduced Sub-Optimality and Guaranteed Constraint Satisfaction
Alexander Benvenuti, Brendan Bialy, Miriam Dennis +1
Linear programming is a fundamental tool in a wide range of decision systems. However, without privacy protections, sharing the solution to a linear program may reveal information…
Deceptive Sequential Decision-Making via Regularized Policy Optimization
Yerin Kim, Alexander Benvenuti, Bo Chen +5
Autonomous systems are increasingly expected to operate in the presence of adversaries, though adversaries may infer sensitive information simply by observing a system. Therefore,…