16 citations · 22 across the 7 of their papers we have counts for
4 papers · 1 filter
Sentence-level Privacy for Document Embeddings
Casey Meehan, Khalil Mrini, Kamalika Chaudhuri
User language data can contain highly sensitive personal content. As such, it is imperative to offer users a strong and interpretable privacy guarantee when learning from their dat…
A Shuffling Framework for Local Differential Privacy
Casey Meehan, Amrita Roy Chowdhury, Kamalika Chaudhuri +1
ldp deployments are vulnerable to inference attacks as an adversary can link the noisy responses to their identity and subsequently, auxiliary information using the order of the da…
A Non-Parametric Test to Detect Data-Copying in Generative Models
Casey Meehan, Kamalika Chaudhuri, Sanjoy Dasgupta
Detecting overfitting in generative models is an important challenge in machine learning. In this work, we formalize a form of overfitting that we call {\em{data-copying}} -- where…
Location Trace Privacy Under Conditional Priors
Casey Meehan, Kamalika Chaudhuri
Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due t…