A Three Sample Hypothesis Test for Evaluating Generative Models. - Casey Meehan; Kamalika Chaudhuri; Sanjoy Dasgupta (AISTATS, 2020)

A Non-Parametric Test to Detect Data-Copying in Generative Models. - Casey Meehan; Kamalika Chaudhuri; Sanjoy Dasgupta (CoRR, 2020)

Location Trace Privacy Under Conditional Priors. - Casey Meehan; Kamalika Chaudhuri (AISTATS, 2021)

A Shuffling Framework for Local Differential Privacy. - Casey Meehan; Amrita Roy Chowdhury 001; Kamalika Chaudhuri; Somesh Jha (CoRR, 2021)

Sentence-level Privacy for Document Embeddings. - Casey Meehan; Khalil Mrini; Kamalika Chaudhuri (ACL (1), 2022)

Privacy Amplification by Subsampling in Time Domain. - Tatsuki Koga; Casey Meehan; Kamalika Chaudhuri (AISTATS, 2022)

Privacy Implications of Shuffling. - Casey Meehan; Amrita Roy Chowdhury 001; Kamalika Chaudhuri; Somesh Jha (ICLR, 2022)

Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning. - Casey Meehan; Florian Bordes; Pascal Vincent; Kamalika Chaudhuri; Chuan Guo 001 (NeurIPS, 2023)

The Primacy of Applied Privacy: Different Approaches to Data Privacy Needed for Different Machine Learning Tasks - Casey Meehan (2023)

Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD. - Anvith Thudi; Hengrui Jia 001; Casey Meehan; Ilia Shumailov; Nicolas Papernot (USENIX Security Symposium, 2024)

Measuring Privacy Loss in Distributed Spatio-Temporal Data. - Tatsuki Koga; Casey Meehan; Kamalika Chaudhuri (CoRR, 2024)