207 citations · 453 across the 21 of their papers we have counts for
8 papers · 1 filter
Bias and Variance of Post-processing in Differential Privacy
Keyu Zhu, Pascal Van Hentenryck, Ferdinando Fioretto
Post-processing immunity is a fundamental property of differential privacy: it enables the application of arbitrary data-independent transformations to the results of differentiall…
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck
A critical concern in data-driven decision making is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ens…
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
Minas Chatzos, Ferdinando Fioretto, Terrence W. K. Mak +1
The AC Optimal Power Flow (AC-OPF) is a key building block in many power system applications. It determines generator setpoints at minimal cost that meet the power demands while sa…
Differentially Private Convex Optimization with Feasibility Guarantees
Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck +2
This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints.…
Differential Privacy of Hierarchical Census Data: An Optimization Approach
Ferdinando Fioretto, Pascal Van Hentenryck, Keyu Zhu
This paper is motivated by applications of a Census Bureau interested in releasing aggregate socio-economic data about a large population without revealing sensitive information ab…
Differentially Private Optimal Power Flow for Distribution Grids
Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck +2
Although distribution grid customers are obliged to share their consumption data with distribution system operators (DSOs), a possible leakage of this data is often disregarded in…