research
∙
10/09/2020
Bias and Variance of Post-processing in Differential Privacy
Post-processing immunity is a fundamental property of differential priva...
share
research
∙
09/26/2020
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
A critical concern in data-driven decision making is to build models who...
research
∙
06/29/2020
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
The AC Optimal Power Flow (AC-OPF) is a key building block in many power...
research
∙
06/28/2020
Differential Privacy of Hierarchical Census Data: An Optimization Approach
This paper is motivated by applications of a Census Bureau interested in...
research
∙
06/22/2020
Differentially Private Convex Optimization with Feasibility Guarantees
This paper develops a novel differentially private framework to solve co...
research
∙
04/08/2020
Differentially Private Optimal Power Flow for Distribution Grids
Although distribution grid customers are obliged to share their consumpt...
research
∙
02/01/2020
Differential Privacy for Stackelberg Games
This paper introduces a differentially private (DP) mechanism to protect...
research
∙
01/26/2020
Bilevel Optimization for Differentially Private Optimization
This paper studies how to apply differential privacy to constrained opti...
research
∙
01/26/2020
A Lagrangian Dual Framework for Deep Neural Networks with Constraints
A variety of computationally challenging constrained optimization proble...
research
∙
11/22/2019
PPSM: A Privacy-Preserving Stackelberg Mechanism: Privacy Guarantees for the Coordination of Sequential Electricity and Gas Markets
This paper introduces a differentially private mechanism to protect the ...
research
∙
10/07/2019
Privacy-Preserving Obfuscation for Distributed Power Systems
This paper considers the problem of releasing privacy-preserving load da...
research
∙
09/19/2019
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
The Optimal Power Flow (OPF) problem is a fundamental building block for...
research
∙
05/23/2019
Privacy-Preserving Obfuscation of Critical Infrastructure Networks
The paper studies how to release data about a critical infrastructure ne...
research
∙
01/21/2019
Differential Privacy for Power Grid Obfuscation
The availability of high-fidelity energy networks brings significant val...
research
∙
08/06/2018
OptStream: Releasing Time Series Privately
Many applications of machine learning and optimization operate on data s...
research
∙
08/06/2018
Differential Private Stream Processing of Energy Consumption
A number of applications benefit from continuously releasing streams of ...
research
∙
02/22/2017
A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
The field of Distributed Constraint Optimization has gained momentum in ...
research
∙
02/22/2017
Solving DCOPs with Distributed Large Neighborhood Search
The field of Distributed Constraint Optimization has gained momentum in ...
research
∙
02/20/2016
