Speaker: İlker Birbil, University of Amsterdam
Date & Time: January 4, 2022, Tuesday, 13:30
Title: Differential Privacy in Multi-Party Resource Sharing
***This is an online event. To obtain Zoom link and password, please contact to the department.
Abstract: This study examines a resource-sharing problem involving multiple parties that agree to use a set of capacities together. We start with modeling the whole problem as a mathematical program, where all parties are required to exchange information to obtain the optimal objective function value. This information bears private data from each party in terms of coefficients used in the mathematical program. Moreover, the parties also consider the individual optimal solutions as private. In this setting, the great concern for the parties is the privacy of their data and their optimal allocations. We propose a two-step approach to meet the privacy requirements of the parties. In the first step, we obtain a reformulated model that is amenable to a decomposition scheme. Although this scheme eliminates almost all data exchange, it does not provide a formal privacy guarantee. In the second step, we provide this guarantee with a differentially private algorithm at the expense of deviating slightly from the optimality. We provide bounds on this deviation, and discuss the consequences of these theoretical results. The study ends with a simulation study on a planning problem that demonstrates an application of the proposed approach.
Bio: İlker Birbil is a professor of AI & Optimization Techniques for Business & Society in University of Amsterdam (UvA), where he is a faculty member at the Business Analytics section of the Amsterdam Business School (ABS). In the past, he had served for three years as a professor of Data Science and Optimization at the Department of Econometrics of Erasmus University (EUR), and before EUR, he had been a professor of optimization at the Industrial Engineering Department of Sabancı University for more than a decade. His research interests center around optimization methods in data science and decision making. Lately, he is interested in interpretable machine learning, and data privacy in operation.