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Full Convergence of the Iterative Bayesian Update and Applications to Mechanisms for Privacy Protection
Ehab ElSalamouny, Catuscia Palamidessi
The iterative Bayesian update (IBU) and the matrix inversion (INV) are the main methods to retrieve the original distribution from noisy data resulting from the application of priv…
Utility-Preserving Privacy Mechanisms for Counting Queries
Natasha Fernandes, Kacem Lefki, Catuscia Palamidessi
Differential privacy (DP) and local differential privacy (LPD) are frameworks to protect sensitive information in data collections. They are both based on obfuscation. In DP the no…
Optimal Obfuscation Mechanisms via Machine Learning
Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi
We consider the problem of obfuscating sensitive information while preserving utility, and we propose a machine learning approach inspired by the generative adversarial networks pa…
F-BLEAU: Fast Black-box Leakage Estimation
Giovanni Cherubin, Konstantinos Chatzikokolakis, Catuscia Palamidessi
We consider the problem of measuring how much a system reveals about its secret inputs. We work under the black-box setting: we assume no prior knowledge of the system's internals,…