{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:36:17Z","timestamp":1783010177552,"version":"3.54.5"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2022,4,9]],"date-time":"2022-04-09T00:00:00Z","timestamp":1649462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"U.S. Census Bureau under CRADA","award":["CB16ADR0160002"],"award-info":[{"award-number":["CB16ADR0160002"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Priv. Secur."],"published-print":{"date-parts":[[2022,8,31]]},"abstract":"<jats:p>\n            Using techniques employing\n            <jats:italic>smooth sensitivity<\/jats:italic>\n            , we develop a method for\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( k \\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            -nearest neighbor missing data imputation with differential privacy. This requires bounding the number of data incomplete tuples that can have their data complete \u201cdonor\u201d changed by making a single addition or deletion to the dataset. The multiplicity of a single individual\u2019s impact on an imputed dataset necessarily means our mechanisms require the addition of more noise than mechanisms that ignore missing data, but we show empirically that this is significantly outweighed by the bias reduction from imputing missing data.\n          <\/jats:p>","DOI":"10.1145\/3507952","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T11:39:29Z","timestamp":1648553969000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Differentially Private\n            <i>k<\/i>\n            -Nearest Neighbor Missing Data Imputation"],"prefix":"10.1145","volume":"25","author":[{"given":"Chris","family":"Clifton","sequence":"first","affiliation":[{"name":"Purdue University, West Lafayette, IN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8096-590X","authenticated-orcid":false,"given":"Eric J.","family":"Hanson","sequence":"additional","affiliation":[{"name":"Laboratoire de Combinatoire et d\u2019Informatique Math\u00e9matique, Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al, Montr\u00e9al, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7288-1973","authenticated-orcid":false,"given":"Keith","family":"Merrill","sequence":"additional","affiliation":[{"name":"Brandeis University, Waltham, MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shawn","family":"Merrill","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,4,9]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"462","volume-title":"Proceedings of the Survey Research Methods Section","author":"III John C. Bailar","year":"1978","unstructured":"John C. Bailar III and Barbara A. Bailar. 1978. Comparison of two procedures for imputing missing survey values. In Proceedings of the Survey Research Methods Section. American Statistical Association, 462\u2013467. Retrieved from http:\/\/www.asasrms.org\/Proceedings."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1162\/003465300556995"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30576-7_20"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3190577"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.01.034"},{"key":"e_1_3_2_8_2","volume-title":"Private Exploration Primitives for Data Cleaning","author":"Ge Chang","year":"2018","unstructured":"Chang Ge, Ihab F. Ilyas, Xi He, and Ashwin Machanavajjhala. 2018. Private Exploration Primitives for Data Cleaning. Technical Report."},{"key":"e_1_3_2_9_2","first-page":"438","volume-title":"Proceedings of the Algorithmic Learning Theory","author":"Gonem Alon","year":"2018","unstructured":"Alon Gonem and Ram Gilad-Bachrach. 2018. Smooth sensitivity based approach for differentially private PCA. In Proceedings of the Algorithmic Learning Theory. 438\u2013450. Retrieved from http:\/\/proceedings.mlr.press\/v83\/gonem18a.html."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622249"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2007.06.013"},{"key":"e_1_3_2_12_2","first-page":"2220","volume-title":"Proceedings of the 35th International Conference on Machine Learning.","author":"Jain Prateek","year":"2018","unstructured":"Prateek Jain, Om Dipakbhai Thakkar, and Abhradeep Thakurta. 2018. Differentially private matrix completion revisited. In Proceedings of the 35th International Conference on Machine Learning.Jennifer G. Dy and Andreas Krause (Eds.), PMLR, 2220\u20132229. Retrieved from http:\/\/proceedings.mlr.press\/v80\/jain18b.html."},{"key":"e_1_3_2_13_2","first-page":"22","volume-title":"Proceedings of the Survey Research Methods Section","author":"Kalton Graham","year":"1982","unstructured":"Graham Kalton and Daniel Kasprzyk. 1982. Imputing for missing survey responses. In Proceedings of the Survey Research Methods Section. American Statistical Association, 22\u201333. Retrieved from http:\/\/www.asasrms.org\/Proceedings."},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973105.101"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2915248"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.14778\/3231751.3231769"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557090"},{"key":"e_1_3_2_18_2","unstructured":"Minnesota Population Center. 2018. Introduction to data editing and allocation. Retrieved from https:\/\/usa.ipums.org\/usa\/flags.shtml."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-985X.2006.00421.x"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/1250790.1250803"},{"key":"e_1_3_2_21_2","unstructured":"Kobbi Nissim Sofya Raskhodnikova and Adam Smith. 2011. Smooth sensitivity and sampling in private data analysis. (May 17 2011). Retrieved from https:\/\/cs-people.bu.edu\/ads22\/pubs\/NRS07\/NRS07-full-draft-v1.pdf."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23525-7_28"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588575"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.18128\/D010.V8.0.EXT1940USCB"},{"key":"e_1_3_2_25_2","volume-title":"American Community Survey Design and Methodology (January 2014)","author":"United States Census Bureau","year":"2014","unstructured":"United States Census Bureau 2014. American Community Survey Design and Methodology (January 2014). Technical Report Version 2.0. United States Census Bureau. Retrieved from https:\/\/www.census.gov\/programs-surveys\/acs\/methodology\/design-and-methodology.html."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.5555\/2612167.2612168"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2021-0077"}],"container-title":["ACM Transactions on Privacy and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3507952","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3507952","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:17Z","timestamp":1750183817000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3507952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,9]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,8,31]]}},"alternative-id":["10.1145\/3507952"],"URL":"https:\/\/doi.org\/10.1145\/3507952","relation":{},"ISSN":["2471-2566","2471-2574"],"issn-type":[{"value":"2471-2566","type":"print"},{"value":"2471-2574","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,9]]},"assertion":[{"value":"2021-07-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}