Understanding Database Reconstruction Attacks on Public Data

被引:39
作者
Garfinkel, Simson [1 ,2 ]
Abowd, John M. [3 ,4 ,5 ]
Martindale, Christian [6 ]
机构
[1] US Census Bur, Confidential & Data Access, Suitland, MD 20746 USA
[2] Census Bur Disclosure Review Board, Washington, DC 20233 USA
[3] US Census Bur, Res & Methodol, Suitland, MD USA
[4] Cornell Univ, Informat Sci, Ithaca, NY USA
[5] Cornell Univ, Dept Stat Sci, Ithaca, NY USA
[6] Duke Univ, Durham, NC USA
关键词
D O I
10.1145/3287287
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
IN 2020, THE U.S. Census Bureau will conduct the Constitutionally mandated decennial Census of Population and Housing. Because a census involves collecting large amounts of private data under the promise of confidentiality, traditionally statistics are published only at high levels of aggregation. Published statistical tables are vulnerable to database reconstruction attacks (DRAs), in which the underlying microdata is recovered merely by finding a set of microdata that is consistent with the published statistical tabulations. A DRA can be performed by using the tables to create a set of mathematical constraints and then solving the resulting set of simultaneous equations. This article shows how such an attack can be addressed by adding noise to the published tabulations, so the reconstruction no longer results in the original data. This has implications for the 2020 census. © 2019 ACM.
引用
收藏
页码:46 / 53
页数:8
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