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Title: Confidence-ranked reconstruction of census microdata from published statistics
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 )∈ℝ m 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.  more » « less
Award ID(s):
2147212 2217062
PAR ID:
10426399
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
120
Issue:
8
ISSN:
0027-8424
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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