RESUMEN
A reconstruction attack on a private dataset D takes as input some publicly accessible information about the dataset and produces a list of candidate elements of D. 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 D from aggregate query statistics Q(D)∈âm 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. We also design a sequence of baselines for evaluating reconstruction attacks. Our attacks significantly outperform those that are based only on access to a public distribution or population from which the private dataset D was sampled, demonstrating that they are exploiting information in the aggregate statistics Q(D) and not simply the overall structure of the distribution. In other words, the queries Q(D) are permitting reconstruction of elements of this dataset, not the distribution from which D was drawn. These findings are established both on 2010 US decennial Census data and queries and Census-derived American Community Survey datasets. Taken together, our methods and experiments illustrate the risks in releasing numerically precise aggregate statistics of a large dataset and provide further motivation for the careful application of provably private techniques such as differential privacy.
RESUMEN
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. But, if we make the more realistic assumption that the published estimates are themselves normally distributed around the "true" number of children in poverty, Cui et al.'s proposed framework allows us to reliably estimate lost entitlements relative to the unknown, ideal allocation of funds-what districts would have received if we knew the "true" number of children in poverty.
RESUMEN
Funding formula reform may help address unequal impacts of uncertainty from data error and privacy protections.