{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T04:34:35Z","timestamp":1771043675236,"version":"3.50.1"},"reference-count":16,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>\n            Across many application domains, trusted parties who collect sensitive information need mechanisms to safely disseminate data. A favored approach is to generate\n            <jats:italic>synthetic data<\/jats:italic>\n            : a dataset similar to the original, hopefully retaining its statistical features, but one that does not reveal the private information of contributors to the data.\n          <\/jats:p>\n          <jats:p>We present PSynDB, a web-based synthetic table generator that is built on recent privacy technologies [10,11,15]. PSynDB satisfies the formal guarantee of differential privacy and generates synthetic tables with high accuracy for tasks that the user specifies as important. PSynDB allows users to browse expected error rates before running the mechanism, a useful feature for making important policy decisions, such as setting the privacy loss budget. When the user has finished configuration, the tool outputs a data synthesis program that can be ported to a trusted environment. There it can be safely executed on the private data to produce the private synthetic dataset for broad dissemination.<\/jats:p>","DOI":"10.14778\/3352063.3352099","type":"journal-article","created":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T18:36:11Z","timestamp":1568831771000},"page":"1918-1921","source":"Crossref","is-referenced-by-count":4,"title":["PSynDB"],"prefix":"10.14778","volume":"12","author":[{"given":"Zhiqi","family":"Huang","sequence":"first","affiliation":[{"name":"Univ. of Massachusetts"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan","family":"McKenna","sequence":"additional","affiliation":[{"name":"Univ. of Massachusetts"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Bissias","sequence":"additional","affiliation":[{"name":"Univ. of Massachusetts"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerome","family":"Miklau","sequence":"additional","affiliation":[{"name":"Univ. of Massachusetts"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Hay","sequence":"additional","affiliation":[{"name":"Colgate University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashwin","family":"Machanavajjhala","sequence":"additional","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Apple Machine Learning Journal","author":"Privacy Team Apple Differential","year":"2017","unstructured":"Apple Differential Privacy Team . Learning with privacy at scale . Apple Machine Learning Journal , 2017 . Apple Differential Privacy Team. Learning with privacy at scale. Apple Machine Learning Journal, 2017."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2450142.2450148"},{"key":"e_1_2_1_3_1","volume-title":"Census of Population and Housing","author":"Census Summary","year":"2012","unstructured":"2010 Census Summary File 1 , Census of Population and Housing , 2012 . 2010 Census Summary File 1, Census of Population and Housing, 2012."},{"key":"e_1_2_1_4_1","volume-title":"UCI machine learning repository","author":"Dheeru D.","year":"2017","unstructured":"D. Dheeru and E. Karra Taniskidou . UCI machine learning repository , 2017 . D. Dheeru and E. Karra Taniskidou. UCI machine learning repository, 2017."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/773153.773173"},{"key":"e_1_2_1_6_1","volume-title":"Calibrating noise to sensitivity in private data analysis","author":"Dwork C.","year":"2006","unstructured":"C. Dwork , F. M. K. Nissim , and A. Smith . Calibrating noise to sensitivity in private data analysis . 2006 . C. Dwork, F. M. K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. 2006."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660348"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807085.1807104"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3231751.3231769"},{"key":"e_1_2_1_11_1","volume-title":"ICML","author":"McKenna R.","year":"2019","unstructured":"R. McKenna , D. Sheldon , and G. Miklau . Graphical-model based estimation and inference for differential privacy . In ICML , 2019 . R. McKenna, D. Sheldon, and G. Miklau. Graphical-model based estimation and inference for differential privacy. In ICML, 2019."},{"key":"e_1_2_1_12_1","unstructured":"OnTheMap Web Tool. http:\/\/onthemp.ces.census.gov\/.  OnTheMap Web Tool. http:\/\/onthemp.ces.census.gov\/."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3085504.3091117"},{"issue":"4","key":"e_1_2_1_14_1","first-page":"531","article-title":"Satisfying disclosure restrictions with synthetic data sets","volume":"18","author":"Reiter J. P.","year":"2002","unstructured":"J. P. Reiter . Satisfying disclosure restrictions with synthetic data sets . Journal of Official Statistics , 18 ( 4 ): 531 , 2002 . J. P. Reiter. Satisfying disclosure restrictions with synthetic data sets. Journal of Official Statistics, 18(4):531, 2002.","journal-title":"Journal of Official Statistics"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196921"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3134428"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3352063.3352099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:33:17Z","timestamp":1672223597000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3352063.3352099"}},"subtitle":["accurate and accessible private data generation"],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":16,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["10.14778\/3352063.3352099"],"URL":"https:\/\/doi.org\/10.14778\/3352063.3352099","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2019,8]]}}}