Publications (9)
Differentially Private Sampling from Distributions
Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith +1
We initiate an investigation of private sampling from distributions. Given a dataset with independent observations from an unknown distribution , a sampling algorithm must o…
Enforcing Demographic Coherence: A Harms Aware Framework for Reasoning about Private Data Release
Mark Bun, Marco Carmosino, Palak Jain +2
The technical literature about data privacy largely consists of two complementary approaches: formal definitions of conditions sufficient for privacy preservation and attacks that…
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization
Mark Bun, Marco Gaboardi, Max Hopkins +5
The notion of replicable algorithms was introduced in Impagliazzo et al. [STOC '22] to describe randomized algorithms that are stable under the resampling of their inputs. More pre…
The Price of Differential Privacy under Continual Observation
Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar +1
We study the accuracy of differentially private mechanisms in the continual release model. A continual release mechanism receives a sensitive dataset as a stream of inputs and…
Improved Accuracy for Private Continual Cardinality Estimation in Fully Dynamic Streams via Matrix Factorization
Joel Daniel Andersson, Palak Jain, Satchit Sivakumar
We study differentially-private statistics in the fully dynamic continual observation model, where many updates can arrive at each time step and updates to a stream can involve bot…
Instance-Optimal Private Density Estimation in the Wasserstein Distance
Vitaly Feldman, Audra McMillan, Satchit Sivakumar +1
Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric fo…