{"id":303,"date":"2018-06-02T22:34:01","date_gmt":"2018-06-03T02:34:01","guid":{"rendered":"http:\/\/pages.vassar.edu\/jihu\/?page_id=303"},"modified":"2025-03-21T11:51:16","modified_gmt":"2025-03-21T15:51:16","slug":"scholarly-research","status":"publish","type":"page","link":"https:\/\/pages.vassar.edu\/jihu\/research\/scholarly-research\/","title":{"rendered":"Scholarly research"},"content":{"rendered":"

My research interests lie in developing Bayesian statistical methodology for applied problems that intersect with the social sciences.\u00a0In particular, I focus on Bayesian methodology on how to help statistical agencies to release their collected microdata from surveys to the public, in a useful and safe way. Through the synthetic data approach, data disseminators can generate simulated data from statistical models estimated from the confidential data, and release synthetic data to the public. I am also working on differential privacy approaches.<\/p>\n

Peer-reviewed publications:<\/b><\/p>\n

* indicates an undergraduate student co-author<\/p>\n

    \n
  1. \u00a0Hu, J.<\/strong>, Williams, M. R. and Savitsky, T. D. (2025), Mechanisms for global differential privacy under Bayesian data synthesis, to appear in Data Privacy special issue<\/em> at Statistica Sinica<\/em>, 35, 563-584.<\/li>\n
  2. Hu, J. <\/strong>and Bowen, C. M. (2024), Advancing microdata privacy protection: a review of synthetic data methods, WIREs Computational Statistics<\/em>, e1636. doi:10.1002\/wics.1636.<\/li>\n
  3. Hu, J.<\/strong> and Savitsky, T. D. (2023), Bayesian data synthesis and disclosure risk quantification: an application to the Consumer Expenditure Surveys, Transactions on Data Privacy<\/i>, 16:2, 83-121.<\/li>\n
  4. Guo, S.* and\u00a0Hu, J.\u00a0<\/strong>(2023), Data privacy protection and utility preservation through Bayesian data synthesis: a case study on Airbnb listings, The American Statistician<\/em>, 77(2), 192-200.\u00a0link to the published paper<\/a><\/li>\n
  5. Schneider, M. J,\u00a0Hu, J.<\/strong>, Mankad, S. and Bale, C. D. (2023), Protecting the anonymity of online users through Bayesian data synthesis, Expert Systems With Applications<\/em>, 216, 119409.\u00a0ResearchGate link<\/a><\/li>\n
  6. Hu, J.<\/strong>, Savitsky, T. D. and Williams, M. R. (2022), Risk-efficient Bayesian pseudo posterior data synthesis for privacy protection,\u00a0Journal of Survey Statistics and Methodology, <\/i>10(5), 1370-1399.\u00a0link to the published paper<\/a><\/li>\n
  7. Cao, Y.* and\u00a0Hu, J.\u00a0<\/strong>(2022), Privacy protection for youth risk behavior using Bayesian data synthesis: a case study to the YRBS<\/a>, Privacy in Statistical Databases e-proceedings.<\/em><\/li>\n
  8. Hu, J.<\/strong>, Drechsler, J. and Kim, H. J. (2022), Accuracy gains from privacy amplification through sampling for differential privacy,\u00a0Journal of Survey Statistics and Methodology, Special Issue: Privacy, Confidentiality, and Disclosure Protection<\/em>, 10(3), 688-719. link to the published paper<\/a><\/li>\n
  9. Hu, J.<\/strong>, Savitsky, T. D. and Williams, M. R. (2022), Private tabular survey data products through synthetic microdata generation,\u00a0Journal of Survey Statistics and Methodology, Special Issue: Privacy, Confidentiality, and Disclosure Protection<\/em>, 10(3), 720-752.\u00a0link to the published paper<\/a><\/li>\n
  10. Savitsky, T. D., Williams, M. R. and\u00a0Hu, J. <\/strong>(2022), Bayesian pseudo posterior mechanism under asymptotic differential privacy, Journal of Machine Learning Research,\u00a0<\/em>23(55), 1\u221237.\u00a0Open Access<\/a><\/li>\n
  11. Hu, J.<\/strong>,\u00a0Akande, O. and Wang, Q. (2021), Multiple imputation and synthetic data generation with the R package NPBayesImputeCat, The R Journal<\/em>,\u00a013:2, 90-110. Open Access<\/a><\/li>\n
  12. Drechsler, J. and\u00a0Hu, J. <\/strong>(2021), Synthesizing geocodes to facilitate access to detailed geographical information in large scale administrative data,\u00a0Journal of Survey Statistics and Methodology,<\/i>\u00a09(3), 523-548.\u00a0Open Access<\/a><\/li>\n
  13. Hornby, R.* and\u00a0Hu, J.\u00a0<\/strong>(2021),\u00a0Identification risks evaluation of partially synthetic data with the IdentificationRiskCalculation R package,\u00a0Transactions on Data Privacy,\u00a0<\/em>14:1, 37-52.\u00a0Open Access<\/a><\/li>\n
  14. Hu, J.<\/strong>, Savitsky, T. D. and Williams, M. R. (2020), Risk-weighted data synthesizers for microdata dissemination,\u00a0Special Issue: A New Generation of Statisticians Tackles Data Privacy, CHANCE, <\/em>33(4), 29-36. Open Access<\/a><\/li>\n
  15. Ros, K.*, Olsson, H.* and Hu, J.<\/strong> (2020),\u00a0Two-phase data synthesis for income: an application to the NHIS, Privacy in Statistical Databases e-proceedings<\/em>. link<\/a><\/li>\n
  16. Hu, J. <\/strong>(2019), Bayesian estimation of attribute and identification disclosure risks in synthetic data,\u00a0Transactions on Data Privacy,\u00a0<\/em>12:1, 61-89. Open Access<\/a><\/li>\n
  17. Hu, J.\u00a0<\/strong>and\u00a0Hoshino, N. (2018), The Quasi-Multinomial synthesizer for categorical data<\/a>,\u00a0\u00a0Privacy in Statistical Databases, Lecture Notes in Computer Science 11126<\/i>\u00a0ed. J. Domingo-Ferrer and F. Montes, Springer, 75-91.<\/li>\n
  18. Manrique-Vallier, D. and\u00a0Hu, J<\/strong>. (2018),\u00a0Bayesian non-parametric generation of synthetic multivariate categorical data in the presence of structural zeros,\u00a0Journal of the Royal Statistical Society, Series A (Statistics in Society), 181(3), 635-647<\/i>.<\/em><\/li>\n
  19. Hu, J.<\/strong>, Reiter, J. P. and Wang, Q. (2018), Dirichlet Process mixture models for modeling and generating synthetic versions of nested categorical data,\u00a0Bayesian Analysis<\/em>, 13(1), 183-200.\u00a0Open Access<\/a>; see software page for\u00a0NestedCategBayesImpute<\/a>\u00a0for method implementation.<\/li>\n
  20. Hu, J.\u00a0<\/strong>and Drechsler, J. (2015),\u00a0Generating synthetic geocoding information for public release,\u00a0In: S. A. Europ\u00e4ische Kommission (Hrsg.), NTTS – Conferences on New Techniques and Technologies for Statistics,\u00a0<\/em>56-59.<\/li>\n
  21. Hu, J.<\/strong>, Reiter, J. P. and Wang, Q. (2014), Disclosure risk evaluation for fully synthetic categorical data<\/a>,\u00a0Privacy in Statistical Databases, Lecture Notes in Computer Science 8744<\/i>\u00a0ed. J. Domingo-Ferrer, Springer, 185-199.; see software page for\u00a0NPBayesImputeCat<\/a>\u00a0for method implementation.<\/li>\n
  22. Hu, J.<\/strong>, Mitra, R. and Reiter, J. P. (2013), Are independent parameter draws necessary for multiple imputation?\u00a0The American Statistician<\/i>. 67(3), 143-149.<\/li>\n
  23. Hu, J.\u00a0<\/strong>and Reiter, J. P. (2013),\u00a0Non-parametric Bayesian model for generating synthetic household data<\/a>,\u00a0Joint UNECE\/Eurostat Work Session on Statistical Data Condentiality 2013.<\/em><\/li>\n<\/ol>\n
    \n

    Technical reports:<\/b><\/p>\n

    * indicates an undergraduate student co-author<\/p>\n

      \n
    1. Hornby, R.* and\u00a0Hu, J.<\/strong>, Bayesian estimation of attribute disclosure risks in synthetic data with the AttributeRiskCalculation R package.\u00a0arXiv link<\/a><\/li>\n<\/ol>\n
      \n

      Work in progress:<\/b><\/p>\n

      * indicates an undergraduate student co-author<\/p>\n

        \n
      1. Savitsky, T. D.,\u00a0Hu, J.<\/strong> and Williams, M. R., Maximizing utility for vector-weighted pseudo posterior mechanisms under differential privacy, submitted. arXiv link<\/a><\/li>\n
      2. Immerwahr, S., Hu, J.<\/strong>, Deng, W. Q., Bholanath, T., Lundy De La Cruz, N. and He, F., \u201cDisclosure risk evaluation and mitigation solutions for health survey microdata files: application to the New York City Community Health Survey\u201d, submitted.\n
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        <\/div>\n<\/div>\n<\/div>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

        My research interests lie in developing Bayesian statistical methodology for applied problems that intersect with the social sciences.\u00a0In particular, I focus on Bayesian methodology on how to help statistical agencies to release their collected microdata from surveys to the public, in a useful and safe way. Through the synthetic data approach, data disseminators can generate … Continue reading Scholarly research<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":5309,"featured_media":0,"parent":22,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-303","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pages.vassar.edu\/jihu\/wp-json\/wp\/v2\/pages\/303","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pages.vassar.edu\/jihu\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pages.vassar.edu\/jihu\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pages.vassar.edu\/jihu\/wp-json\/wp\/v2\/users\/5309"}],"replies":[{"embeddable":true,"href":"https:\/\/pages.vassar.edu\/jihu\/wp-json\/wp\/v2\/comments?post=303"}],"version-history":[{"count":125,"href":"https:\/\/pages.vassar.edu\/jihu\/wp-json\/wp\/v2\/pages\/303\/revisions"}],"predecessor-version":[{"id":1007,"href":"https:\/\/pages.vassar.edu\/jihu\/wp-json\/wp\/v2\/pages\/303\/revisions\/1007"}],"up":[{"embeddable":true,"href":"https:\/\/pages.vassar.edu\/jihu\/wp-json\/wp\/v2\/pages\/22"}],"wp:attachment":[{"href":"https:\/\/pages.vassar.edu\/jihu\/wp-json\/wp\/v2\/media?parent=303"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}