Professor Martina Cardone at ECE Fall 2024 Colloquium

On the performance of ranking recovery algorithms with privacy consideration

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. For instance, a user would not like to disclose their previous purchases to a recommender system. 

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.