4 citations · 5 across the 8 of their papers we have counts for
9 papers
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…
Interactive Proofs For Distribution Testing With Conditional Oracles
Ari Biswas, Mark Bun, Clément Canonne +1
We revisit the framework of interactive proofs for distribution testing, first introduced by Chiesa and Gur (ITCS 2018), which has recently experienced a surge in interest, accompa…
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…
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…
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation
Palak Jain, Iden Kalemaj, Sofya Raskhodnikova +2
Privacy is a central challenge for systems that learn from sensitive data sets, especially when a system's outputs must be continuously updated to reflect changing data. We conside…
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…
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…
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…
Multiclass versus Binary Differentially Private PAC Learning
Mark Bun, Marco Gaboardi, Satchit Sivakumar
We show a generic reduction from multiclass differentially private PAC learning to binary private PAC learning. We apply this transformation to a recently proposed binary private P…