9 citations · 11 across the 5 of their papers we have counts for
4 papers · 1 filter
CALLIOPE: Private Insights into AI Use, Without Embeddings
Edith Cohen, Vadym Doroshenko, Badih Ghazi +11
We introduce CALLIOPE, a top-down framework for discovering topic hierarchies in large corpora of unstructured data (e.g., LLM conversations), while supporting differential privacy…
Tight End-to-End Accounting for Differentially Private Synthetic Data
Georgi Ganev, Vadym Doroshenko, Arun Ganesh +3
Differentially private (DP) pipelines often combine heterogeneous mechanisms, yet their guarantees are best analyzed using different notions of privacy (e.g., bounded range, zCDP,…
Online Differentially Private Consistent Clustering
Edith Cohen, Vadym Doroshenko, Badih Ghazi +7
We study differentially private (DP) -means and -median clustering in the online streaming setting. In this model, points arrive sequentially, and at each time step, we need…
JAX-Privacy: A library for differentially private machine learning
Ryan McKenna, Galen Andrew, Borja Balle +6
JAX-Privacy is a library designed to simplify the deployment of robust and performant mechanisms for differentially private machine learning. Guided by design principles of usabili…