1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
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…