542 citations · 1.6k across the 85 of their papers we have counts for
6 papers · 1 filter
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…
Capacity Bounded Differential Privacy
Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala
Differential privacy, a notion of algorithmic stability, is a gold standard for measuring the additional risk an algorithm's output poses to the privacy of a single record in the d…
Robustness for Non-Parametric Classification: A Generic Attack and Defense
Yao-Yuan Yang, Cyrus Rashtchian, Yizhen Wang +1
Adversarially robust machine learning has received much recent attention. However, prior attacks and defenses for non-parametric classifiers have been developed in an ad-hoc or cla…
The Label Complexity of Active Learning from Observational Data
Songbai Yan, Kamalika Chaudhuri, Tara Javidi
Counterfactual learning from observational data involves learning a classifier on an entire population based on data that is observed conditioned on a selection policy. This work c…
An Investigation of Data Poisoning Defenses for Online Learning
Yizhen Wang, Somesh Jha, Kamalika Chaudhuri
Data poisoning attacks -- where an adversary can modify a small fraction of training data, with the goal of forcing the trained classifier to high loss -- are an important threat f…
Profile-Based Privacy for Locally Private Computations
Joseph Geumlek, Kamalika Chaudhuri
Differential privacy has emerged as a gold standard in privacy-preserving data analysis. A popular variant is local differential privacy, where the data holder is the trusted curat…