Friday, April 4
SEC LL2.221 or Zoom (Passcode: 988031)
11:00am - 12:00pm

On the Performance of Ranking Algorithms with Privacy Considerations
Martina Cardone, Assistant Professor, University of Minnesota

Abstract: Today, ranking algorithms are of fundamental importance and are used in a wide variety of applications, such as recommender systems and search engines. Broadly speaking, the goal of a ranking algorithm is to sort a dataset so that users are provided with accurate and relevant results. Although modern ranking algorithms promise efficient means of performing large-scale data processing, there are numerous privacy considerations that must not be overlooked.
In this talk, we consider the private ranking recovery problem, which consists of recovering the ranking/permutation of an input data vector from a noisy version of it. We aim to establish fundamental trade-offs between the performance of the estimation task, measured in terms of probability of error, and the level of privacy that can be guaranteed when the noise mechanism consists of adding artificial noise.

Speaker Bio: Martina Cardone received her Ph.D. degree in electronics and communications from Télécom ParisTech (with work done at Eurecom in Sophia Antipolis, France) in 2015. She is currently an Assistant Professor with the Electrical and Computer Engineering Department, University of Minnesota (UMN). From July 2015 to August 2017, she was a Postdoctoral Research Fellow with the Electrical and Computer Engineering Department, UCLA Henry Samueli School. Her main research interests are in estimation theory, network information theory, network coding, and wireless networks with a special focus on their capacity, security, and privacy aspects. She is a recipient of the 2022 McKnight Land-Grant Professorship, the NSF CAREER Award in 2021, the NSF CRII Award in 2019, the Outstanding Ph.D. Award from Télécom ParisTech (Paris, France), and the Qualcomm Innovation Fellowship in 2014.