Balancing data privacy and usability in the federal statistical system
The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other...
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| Published in: | Proceedings of the National Academy of Sciences - PNAS Vol. 119; no. 31; p. e2104906119 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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United States
02.08.2022
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| ISSN: | 1091-6490, 1091-6490 |
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| Abstract | The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit-cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs. |
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| AbstractList | The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit-cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs. The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit-cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs.The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit-cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs. |
| Author | Manski, Charles F Hotz, V Joseph Bollinger, Christopher R Nekipelov, Denis Spencer, Bruce D Komarova, Tatiana Moffitt, Robert A Sojourner, Aaron |
| Author_xml | – sequence: 1 givenname: V Joseph orcidid: 0000-0002-6958-3318 surname: Hotz fullname: Hotz, V Joseph organization: Department of Economics, Duke University, Durham, NC 27708 – sequence: 2 givenname: Christopher R orcidid: 0000-0002-3477-1028 surname: Bollinger fullname: Bollinger, Christopher R organization: Department of Economics, University of Kentucky, Lexington, KY 40503 – sequence: 3 givenname: Tatiana orcidid: 0000-0002-6581-5097 surname: Komarova fullname: Komarova, Tatiana organization: The London School of Economics and Political Science, London WC2A 3PH, United Kingdom – sequence: 4 givenname: Charles F orcidid: 0000-0001-7260-7686 surname: Manski fullname: Manski, Charles F organization: Department of Economics, Northwestern University, Evanston, IL 60208 – sequence: 5 givenname: Robert A orcidid: 0000-0002-3627-3057 surname: Moffitt fullname: Moffitt, Robert A organization: Department of Economics, Johns Hopkins University, Baltimore, MD 21211 – sequence: 6 givenname: Denis orcidid: 0000-0003-4734-265X surname: Nekipelov fullname: Nekipelov, Denis organization: Department of Economics, University of Virginia, Charlottesville, VA 22904 – sequence: 7 givenname: Aaron orcidid: 0000-0001-6839-2512 surname: Sojourner fullname: Sojourner, Aaron organization: W. E. Upjohn Institute for Employment Policy, Kalamazoo, MI 49007 – sequence: 8 givenname: Bruce D orcidid: 0000-0001-6155-7249 surname: Spencer fullname: Spencer, Bruce D organization: Department of Statistics and Data Science, Northwestern University, Evanston, IL 60208 |
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| Title | Balancing data privacy and usability in the federal statistical system |
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