239 citations · 502 across the 13 of their papers we have counts for
17 papers
Differentially Private Inference for Binomial Data
Jordan Awan, Aleksandra Slavkovic
We derive uniformly most powerful (UMP) tests for simple and one-sided hypotheses for a population proportion within the framework of Differential Privacy (DP), optimizing finite s…
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA
Jordan Awan, Ana Kenney, Matthew Reimherr +1
The exponential mechanism is a fundamental tool of Differential Privacy (DP) due to its strong privacy guarantees and flexibility. We study its extension to settings with summaries…
pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity
Joshua Snoke, Aleksandra Slavković
We propose a method for the release of differentially private synthetic datasets. In many contexts, data contain sensitive values which cannot be released in their original form in…
Differentially Private Uniformly Most Powerful Tests for Binomial Data
Jordan Awan, Aleksandra Slavkovic
We derive uniformly most powerful (UMP) tests for simple and one-sided hypotheses for a population proportion within the framework of Differential Privacy (DP), optimizing finite s…
Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms
Jordan Awan, Aleksandra Slavkovic
Differential privacy (DP), provides a framework for provable privacy protection against arbitrary adversaries, while allowing the release of summary statistics and synthetic data.…
Providing Accurate Models across Private Partitioned Data: Secure Maximum Likelihood Estimation
Joshua Snoke, Timothy R. Brick, Aleksandra Slavkovic +1
This paper focuses on the privacy paradigm of providing access to researchers to remotely carry out analyses on sensitive data stored behind firewalls. We address the situation whe…
Differentially Private Model Selection with Penalized and Constrained Likelihood
Jing Lei, Anne-Sophie Charest, Aleksandra Slavkovic +2
In statistical disclosure control, the goal of data analysis is twofold: The released information must provide accurate and useful statistics about the underlying population of int…
General and specific utility measures for synthetic data
Joshua Snoke, Gillian Raab, Beata Nowok +2
Data holders can produce synthetic versions of datasets when concerns about potential disclosure restrict the availability of the original records. This paper is concerned with met…
Private Posterior distributions from Variational approximations
Vishesh Karwa, Dan Kifer, Aleksandra B. Slavković
Privacy preserving mechanisms such as differential privacy inject additional randomness in the form of noise in the data, beyond the sampling mechanism. Ignoring this additional no…
Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models
Vishesh Karwa, Pavel N. Krivitsky, Aleksandra B. Slavković
Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in…
Differentially Private Exponential Random Graphs
Vishesh Karwa, Aleksandra B. Slavković, Pavel Krivitsky
We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fittin…
Scalable Privacy-Preserving Data Sharing Methodology for Genome-Wide Association Studies
Fei Yu, Stephen E. Fienberg, Aleksandra Slavković +1
The protection of privacy of individual-level information in genome-wide association study (GWAS) databases has been a major concern of researchers following the publication of "an…
10 Simple Rules for the Care and Feeding of Scientific Data
Alyssa Goodman, Alberto Pepe, Alexander W. Blocker +12
This article offers a short guide to the steps scientists can take to ensure that their data and associated analyses continue to be of value and to be recognized. In just the past…
Fibers of multi-way contingency tables given conditionals: relation to marginals, cell bounds and Markov bases
Aleksandra B. Slavković, Xiaotian Zhu, Sonja Petrović
A reference set, or a fiber, of a contingency table is the space of all realizations of the table under a given set of constraints such as marginal totals. Understanding the geomet…
Privacy-Preserving Data Sharing for Genome-Wide Association Studies
Caroline Uhler, Aleksandra B. Slavkovic, Stephen E. Fienberg
Traditional statistical methods for confidentiality protection of statistical databases do not scale well to deal with GWAS (genome-wide association studies) databases especially i…
Inference using noisy degrees: Differentially private -model and synthetic graphs
Vishesh Karwa, Aleksandra Slavković
The -model of random graphs is an exponential family model with the degree sequence as a sufficient statistic. In this paper, we contribute three key results. First, we characte…
The space of compatible full conditionals is a unimodular toric variety
Aleksandra B Slavkovic, Seth Sullivant
The set of all m-tuples of compatible full conditional distributions on discrete random variables is an algebraic set whose defining ideal is a unimodular toric ideal. We identify…