62 citations · 64 across the 8 of their papers we have counts for
4 papers · 1 filter
Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees
Christian Janos Lebeda, David Erb, Tudor Cebere +1
Random forests are widely used in fields involving sensitive tabular data, but existing approaches to enforcing differential privacy (DP) typically degrade performance to the point…
Privacy Auditing with Zero (0) Training Run
Tudor Cebere, Mathieu Even, Linus Bleistein +1
Privacy auditing provides empirical lower bounds on the differential privacy parameters of learning algorithms. Existing methods, however, require interventional access to the trai…
Privacy in Theory, Bugs in Practice: Grey-Box Auditing of Differential Privacy Libraries
Tudor Cebere, David Erb, Damien Desfontaines +2
Differential privacy (DP) implementations are notoriously prone to errors, with subtle bugs frequently invalidating theoretical guarantees. Existing verification methods are often…
Causal Evaluation of Membership Inference Attacks
Mathieu Even, Clément Berenfeld, Linus Bleistein +3
Membership Inference Attacks (MIAs) aim to distinguish training points (members) from unseen data (non-members), and are widely used to quantify memorization and assess privacy ris…