ALOP-Colloquium with Pascal Van Hentenryck, Georgia Tech

On Monday, May 10, 2021, Prof. Dr. Pascal Van Hentenryck, Georgia Tech will present his recent research titled:

Differential privacy of hierarchical Census data: An optimization approach

This talk considers applications of a Census Bureau interested in releasing aggregate socio-economic data about a large population without revealing sensitive information about any individual. The released information can be the number of individuals living alone, the number of cars they own, or their salary brackets. Recent events have identified some of the privacy challenges faced by these organizations. To address them, we present a novel differential-privacy mechanism for releasing hierarchical counts of individuals. The counts are reported at multiple granularities (e.g., the national, state, and county levels) and must be consistent across all levels. The core of the mechanism is an optimization model that redistributes the noise introduced to achieve differential privacy in order to meet the consistency constraints between the hierarchical levels. The key technical contribution of the paper shows that this optimization problem can be solved in polynomial time by exploiting the structure of its cost functions. Experimental results on very large, real datasets show that the proposed mechanism provides improvements of up to two orders of magnitude in terms of computational efficiency and accuracy with respect to other state-of-the-art techniques. The talk also discusses various fairness issues arising in using differential privacy in downstream applications and how they can be mitigated.

Joint work with Ferdinando Fioretto and Keyu Zhu

This presentation will take place via Zoom. A link to participate in this presentation will be mailed shortly before the event.

If you wish to participate and receive and inviation, please send an email to shawATuni-trier.de.