62 citations · 64 across the 8 of their papers we have counts for
8 papers
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…
Private Rate-Constrained Optimization with Applications to Fair Learning
Mohammad Yaghini, Tudor Cebere, Michael Menart +2
Many problems in trustworthy ML can be expressed as constraints on prediction rates across subpopulations, including group fairness constraints (demographic parity, equalized odds,…
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
Tudor Cebere, Aurélien Bellet, Nicolas Papernot
Machine learning models can be trained with formal privacy guarantees via differentially private optimizers such as DP-SGD. In this work, we focus on a threat model where the adver…
Syft 0.5: A Platform for Universally Deployable Structured Transparency
Adam James Hall, Madhava Jay, Tudor Cebere +20
We present Syft 0.5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems. Th…
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
Daniele Romanini, Adam James Hall, Pavlos Papadopoulos +6
We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on…