1.1k citations · 2.1k across the 73 of their papers we have counts for
6 papers · 1 filter
Experiments \& Analysis of Privacy-Preserving SQL Query Sanitization Systems
Loïs Ecoffet, Veronika Rehn-Sonigo, Jean-François Couchot +1
Analytical SQL queries are essential for extracting insights from relational databases but concurrently introduce significant privacy risks by potentially exposing sensitive inform…
On the Consistency and Performance of the Iterative Bayesian Update
Ehab ElSalamouny, Catuscia Palamidessi
In many situations, estimating the distribution of users' data concerning certain attributes is important. To facilitate this estimation while safeguarding users' privacy, the loca…
Mitigating Membership Inference Vulnerability in Personalized Federated Learning
Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi
Federated Learning (FL) has emerged as a promising paradigm for collaborative model training without the need to share clients' personal data, thereby preserving privacy. However,…
Jeffrey's update rule as a minimizer of Kullback-Leibler divergence
Carlos Pinzón, Catuscia Palamidessi
In this paper, we show a more concise and high level proof than the original one, derived by researcher Bart Jacobs, for the following theorem: in the context of Bayesian update ru…
Comparing privacy notions for protection against reconstruction attacks in machine learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal concern and have been identified even in federated learning (FL), which was designed with privacy pres…
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks
Judith Sáinz-Pardo Díaz, Andreas Athanasiou, Kangsoo Jung +2
Federated learning is a distributed learning technique that allows training a global model with the participation of different data owners without the need to share raw data. This…