IDEAS home Printed from https://ideas.repec.org/a/bes/jnlasa/v105i489y2010p375-389.html
   My bibliography  Save this article

A Statistical Framework for Differential Privacy

Author

Listed:
  • Wasserman, Larry
  • Zhou, Shuheng

Abstract

No abstract is available for this item.

Suggested Citation

  • Wasserman, Larry & Zhou, Shuheng, 2010. "A Statistical Framework for Differential Privacy," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 375-389.
    • Handle: RePEc:bes:jnlasa:v:105:i:489:y:2010:p:375-389
    as

    Download full text from publisher

    File URL: http://pubs.amstat.org/doi/abs/10.1198/jasa.2009.tm08651
    File Function: full text
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.
    2. Claire McKay Bowen & Fang Liu & Bingyue Su, 2021. "Differentially private data release via statistical election to partition sequentially," METRON, Springer;Sapienza Università di Roma, vol. 79(1), pages 1-31, April.
    3. Ryan Cumings-Menon, 2022. "Differentially Private Estimation via Statistical Depth," Papers 2207.12602, arXiv.org.
    4. Vishesh Karwa & Pavel N. Krivitsky & Aleksandra B. Slavković, 2017. "Sharing social network data: differentially private estimation of exponential family random-graph models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 481-500, April.
    5. John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.
    6. Raj Chetty & John N. Friedman, 2019. "A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 414-420, May.
    7. Katherine B. Coffman & Lucas C. Coffman & Keith M. Marzilli Ericson, 2017. "The Size of the LGBT Population and the Magnitude of Antigay Sentiment Are Substantially Underestimated," Management Science, INFORMS, vol. 63(10), pages 3168-3186, October.
    8. Bi, Xuan & Shen, Xiaotong, 2023. "Distribution-invariant differential privacy," Journal of Econometrics, Elsevier, vol. 235(2), pages 444-453.
    9. Ori Heffetz & Katrina Ligett, 2014. "Privacy and Data-Based Research," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 75-98, Spring.
    10. Toth Daniell, 2014. "Data Smearing: An Approach to Disclosure Limitation for Tabular Data," Journal of Official Statistics, Sciendo, vol. 30(4), pages 839-857, December.
    11. Jinshuo Dong & Aaron Roth & Weijie J. Su, 2022. "Gaussian differential privacy," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 3-37, February.
    12. Jing Lei & Anne‐Sophie Charest & Aleksandra Slavkovic & Adam Smith & Stephen Fienberg, 2018. "Differentially private model selection with penalized and constrained likelihood," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 609-633, June.
    13. John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Simson Garfinkel & Micah Heineck & Christine Heiss & Robert Johns & Daniel Kifer & Philip Leclerc & Ashwin Machanavajjhala & Brett Moran & William, 2022. "The 2020 Census Disclosure Avoidance System TopDown Algorithm," Papers 2204.08986, arXiv.org.
    14. Harrison Quick, 2021. "Generating Poisson‐distributed differentially private synthetic data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 1093-1108, July.
    15. Soumya Mukherjee & Aratrika Mustafi & Aleksandra Slavkovi'c & Lars Vilhuber, 2023. "Assessing Utility of Differential Privacy for RCTs," Papers 2309.14581, arXiv.org.
    16. Chongliang Luo & Md. Nazmul Islam & Natalie E. Sheils & John Buresh & Jenna Reps & Martijn J. Schuemie & Patrick B. Ryan & Mackenzie Edmondson & Rui Duan & Jiayi Tong & Arielle Marks-Anglin & Jiang Bi, 2022. "DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    17. Chang, Jinyuan & Hu, Qiao & Kolaczyk, Eric D. & Yao, Qiwei & Yi, Fengting, 2024. "Edge differentially private estimation in the β-model via jittering and method of moments," LSE Research Online Documents on Economics 122099, London School of Economics and Political Science, LSE Library.
    18. Lalanne, Clément & Gadat, Sébastien, 2024. "Privately Learning Smooth Distributions on the Hypercube by Projections," TSE Working Papers 24-1505, Toulouse School of Economics (TSE).
    19. Soumya Mukherjee & Aratrika Mustafi & Aleksandra Slavkovic & Lars Vilhuber, 2024. "Improving Privacy for Respondents in Randomized Controlled Trials: A Differential Privacy Approach," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.
    20. Jinshuo Dong & Aaron Roth & Weijie J. Su, 2022. "Authors’ reply to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 50-54, February.
    21. Kwak, Seung Woo & Ahn, Jeongyoun & Lee, Jaewoo & Park, Cheolwoo, 2024. "Differentially Private Goodness-of-Fit Tests for Continuous Variables," Econometrics and Statistics, Elsevier, vol. 31(C), pages 81-99.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bes:jnlasa:v:105:i:489:y:2010:p:375-389. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.