Response to comment on "Policy impacts of statistical uncertainty and privacy".

We offer our thanks to the authors for their thoughtful comments. Cui, Gong, Hannig, and Hoffman propose a valuable improvement to our method of estimating lost entitlements due to data error. Because we don't have access to the unknown, "true" number of children in poverty, our paper simulates data error by drawing counterfactual estimates from a normal distribution around the official, published poverty estimates, which we use to calculate lost entitlements relative to the official allocation of funds.

Confidence-ranked reconstruction of census microdata from published statistics.

A reconstruction attack on a private dataset takes as input some publicly accessible information about the dataset and produces a list of candidate elements of . We introduce a class of data reconstruction attacks based on randomized methods for nonconvex optimization. We empirically demonstrate that our attacks can not only reconstruct full rows of from aggregate query statistics ()∈ℝ but can do so in a way that reliably ranks reconstructed rows by their odds of appearing in the private data, providing a signature that could be used for prioritizing reconstructed rows for further actions such as identity theft or hate crime.

Policy impacts of statistical uncertainty and privacy.

Funding formula reform may help address unequal impacts of uncertainty from data error and privacy protections.

Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing.

Background: Data sharing accelerates scientific progress but sharing individual-level data while preserving patient privacy presents a barrier.

Methods And Results: Using pairs of deep neural networks, we generated simulated, synthetic participants that closely resemble participants of the SPRINT trial (Systolic Blood Pressure Trial). We showed that such paired networks can be trained with differential privacy, a formal privacy framework that limits the likelihood that queries of the synthetic participants' data could identify a real a participant in the trial.

Private algorithms for the protected in social network search.

Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation). The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks.