3 citations · 5 across the 13 of their papers we have counts for
14 papers
The Cost of Differential Privacy in Linear-Quadratic Dynamic Games
Chih-Yuan Chiu, Matthew Hale
Multi-agent coordination often requires strategic agents to share sensitive information about their states or objectives, creating a tension between performance and privacy. Our pa…
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…
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…
Multi-Agent System Identification with Nonlinear Sheaf Diffusion
Nivar Anwer, Hans Riess, Matthew Hale
Local interaction laws governing multi-agent systems can be difficult to recover from trajectory data, even when the dynamics are observed faithfully. In systems governed by a nonl…
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,…
Guaranteed Feasibility in Differentially Private Linearly Constrained Convex Optimization
Alexander Benvenuti, Brendan Bialy, Miriam Dennis +1
Convex programming with linear constraints plays an important role in the operation of a number of everyday systems. However, absent any additional protections, revealing or acting…
Modeling Epidemic Spread: A Gaussian Process Regression Approach
Baike She, Lei Xin, Philip E. Paré +1
Modeling epidemic spread is critical for informing policy decisions aimed at mitigation. Accordingly, in this work we present a new data-driven method based on Gaussian process reg…
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…