7 citations · 11 across the 8 of their papers we have counts for
8 papers
Assessing Utility of Differential Privacy for RCTs
Kaitlyn R. Webb, Soumya Mukherjee, Aratrika Mustafi +2
Randomized controlled trials (RCTs) have become powerful tools for assessing the impact of interventions and policies in many contexts. They are considered the gold standard for ca…
Shape And Structure Preserving Differential Privacy
Carlos Soto, Karthik Bharath, Matthew Reimherr +1
It is common for data structures such as images and shapes of 2D objects to be represented as points on a manifold. The utility of a mechanism to produce sanitized differentially p…
Perfect Spectral Clustering with Discrete Covariates
Jonathan Hehir, Xiaoyue Niu, Aleksandra Slavkovic
Among community detection methods, spectral clustering enjoys two desirable properties: computational efficiency and theoretical guarantees of consistency. Most studies of spectral…
Statistical Data Privacy: A Song of Privacy and Utility
Aleksandra Slavkovic, Jeremy Seeman
To quantify trade-offs between increasing demand for open data sharing and concerns about sensitive information disclosure, statistical data privacy (SDP) methodology analyzes data…
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms
Jeremy Seeman, Matthew Reimherr, Aleksandra Slavkovic
Implementations of the exponential mechanism in differential privacy often require sampling from intractable distributions. When approximate procedures like Markov chain Monte Carl…
Perturbed M-Estimation: A Further Investigation of Robust Statistics for Differential Privacy
Aleksandra Slavkovic, Roberto Molinari
Differential Privacy (DP) provides an elegant mathematical framework for defining a provable disclosure risk in the presence of arbitrary adversaries; it guarantees that whether an…
Consistent Spectral Clustering of Network Block Models under Local Differential Privacy
Jonathan Hehir, Aleksandra Slavkovic, Xiaoyue Niu
The stochastic block model (SBM) and degree-corrected block model (DCBM) are network models often selected as the fundamental setting in which to analyze the theoretical properties…
Formal Privacy for Functional Data with Gaussian Perturbations
Ardalan Mirshani, Matthew Reimherr, Aleksandra Slavkovic
Motivated by the rapid rise in statistical tools in Functional Data Analysis, we consider the Gaussian mechanism for achieving differential privacy with parameter estimates taking…