4 citations · 4 across the 5 of their papers we have counts for
5 papers
Separating Oblivious and Adaptive Differential Privacy under Continual Observation
Mark Bun, Marco Gaboardi, Connor Wagaman
We resolve an open question of Jain, Raskhodnikova, Sivakumar, and Smith (ICML 2023) by exhibiting a problem separating differential privacy under continual observation in the obli…
Local Node Differential Privacy
Sofya Raskhodnikova, Adam Smith, Connor Wagaman +1
We initiate an investigation of node differential privacy for graphs in the local model of private data analysis. In our model, dubbed LNDP*, each node sees its own edge list and r…
Refereed Learning
Ran Canetti, Ephraim Linder, Connor Wagaman
We initiate an investigation of learning tasks in a setting where the learner is given access to two competing provers, only one of which is honest. Specifically, we consider the p…
Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation
Palak Jain, Adam Smith, Connor Wagaman
We describe the first algorithms that satisfy the standard notion of node-differential privacy in the continual release setting (i.e., without an assumed promise on input streams).…
Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix It
Sílvia Casacuberta, Michael Shoemate, Salil Vadhan +1
We identify a new class of vulnerabilities in implementations of differential privacy. Specifically, they arise when computing basic statistics such as sums, thanks to discrepancie…