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Database Reconstruction Is Not So Easy and Is Different from Reidentification

Author

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  • Muralidhar Krishnamurty

    (University of Oklahoma Price College of Business, Dept. of Marketing and Supply Chain Management, 307 West Brooks, Adams Hall Room 10 Norman, OK 73019, U.S.A.)

  • Domingo-Ferrer Josep

    (Universitat Rovira i Virgili, Department of Computer Engineering and Mathematics, CYBERCAT-Center for Cybersecurity Research of Catalonia Av. Països Catalans 26, 43007 Tarragona, Catalonia.)

Abstract

In recent years, it has been claimed that releasing accurate statistical information on a database is likely to allow its complete reconstruction. Differential privacy has been suggested as the appropriate methodology to prevent these attacks. These claims have recently been taken very seriously by the U.S. Census Bureau and led them to adopt differential privacy for releasing U.S. Census data. This in turn has caused consternation among users of the Census data due to the lack of accuracy of the protected outputs. It has also brought legal action against the U.S. Department of Commerce. In this article, we trace the origins of the claim that releasing information on a database automatically makes it vulnerable to being exposed by reconstruction attacks and we show that this claim is, in fact, incorrect. We also show that reconstruction can be averted by properly using traditional statistical disclosure control (SDC) techniques. We further show that the geographic level at which exact counts are released is even more relevant to protection than the actual SDC method employed. Finally, we caution against confusing reconstruction and reidentification: using the quality of reconstruction as a metric of reidentification results in exaggerated reidentification risk figures.

Suggested Citation

  • Muralidhar Krishnamurty & Domingo-Ferrer Josep, 2023. "Database Reconstruction Is Not So Easy and Is Different from Reidentification," Journal of Official Statistics, Sciendo, vol. 39(3), pages 381-398, September.
    • Handle: RePEc:vrs:offsta:v:39:y:2023:i:3:p:381-398:n:4
    DOI: 10.2478/jos-2023-0017
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    References listed on IDEAS

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    1. John M Abowd & Michael B Hawes, 2022. "Confidentiality Protection in the 2020 US Census of Population and Housing," Papers 2206.03524, arXiv.org, revised Dec 2022.
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    Cited by:

    1. Ron S. Jarmin & John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Nathan Goldschlag & Michael B. Hawes & Sallie Ann Keller & Daniel Kifer & Philip Leclerc & Jerome P. Reiter & Rolando A. Rodrígue, 2023. "An in-depth examination of requirements for disclosure risk assessment," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(43), pages 2220558120-, October.

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