{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T13:20:59Z","timestamp":1770297659266,"version":"3.49.0"},"reference-count":25,"publisher":"Association for Computing Machinery (ACM)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2014,4]]},"abstract":"<jats:p>\n            We present an approach to differentially private computation in which one does not scale up the magnitude of noise for challenging queries, but rather scales\n            <jats:italic>down<\/jats:italic>\n            the contributions of challenging records. While scaling down all records uniformly is equivalent to scaling up the noise magnitude, we show that scaling records\n            <jats:italic>non-uniformly<\/jats:italic>\n            can result in substantially higher accuracy by bypassing the worst-case requirements of differential privacy for the noise magnitudes.\n          <\/jats:p>\n          <jats:p>\n            This paper details the data analysis platform\n            <jats:bold>wPINQ<\/jats:bold>\n            , which generalizes the Privacy Integrated Query (PINQ) to weighted datasets. Using a few simple operators (including a non-uniformly scaling Join operator) wPINQ can reproduce (and improve) several recent results on graph analysis and introduce new generalizations (\n            <jats:italic>e.g.<\/jats:italic>\n            , counting triangles with given degrees). We also show how to integrate probabilistic inference techniques to synthesize datasets respecting more complicated (and less easily interpreted) measurements.\n          <\/jats:p>","DOI":"10.14778\/2732296.2732300","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"637-648","source":"Crossref","is-referenced-by-count":96,"title":["Calibrating data to sensitivity in private data analysis"],"prefix":"10.14778","volume":"7","author":[{"given":"Davide","family":"Proserpio","sequence":"first","affiliation":[{"name":"Boston University"}]},{"given":"Sharon","family":"Goldberg","sequence":"additional","affiliation":[{"name":"Boston University"}]},{"given":"Frank","family":"McSherry","sequence":"additional","affiliation":[{"name":"Microsoft Research"}]}],"member":"320","published-online":{"date-parts":[[2014,4]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1987)","author":"Barab\u00e1si A.-L.","year":"2013","unstructured":"A.-L. Barab\u00e1si . Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1987) , 2013 . A.-L. Barab\u00e1si. Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1987), 2013."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2422436.2422449"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2465304"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2429069.2429113"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/2028067.2028100"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2009.11"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1989323.1989453"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402749"},{"key":"e_1_2_1_10_1","volume-title":"Graph analysis with node-level differential privacy","author":"Kasiviswanathan S.","year":"2012","unstructured":"S. Kasiviswanathan , K. Nissim , S. Raskhodnikova , and A. Smith . Graph analysis with node-level differential privacy , 2012 . S. Kasiviswanathan, K. Nissim, S. Raskhodnikova, and A. Smith. Graph analysis with node-level differential privacy, 2012."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1217299.1217301"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807085.1807104"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/2168651.2168653"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1159913.1159930"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559845.1559850"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213876"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1250790.1250803"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2342549.2342553"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1559795.1559812"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1932681.1863568"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-39718-2_23"},{"key":"e_1_2_1_22_1","first-page":"20","volume-title":"Proc. USENIX NSDI'10","author":"Roy I.","year":"2010","unstructured":"I. Roy , S. T. Setty , A. Kilzer , V. Shmatikov , and E. Witchel . Airavat: security and privacy for mapreduce . In Proc. USENIX NSDI'10 , pages 20 -- 20 . USENIX Association , 2010 . I. Roy, S. T. Setty, A. Kilzer, V. Shmatikov, and E. Witchel. Airavat: security and privacy for mapreduce. In Proc. USENIX NSDI'10, pages 20--20. USENIX Association, 2010."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2068816.2068825"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2012.71"},{"key":"e_1_2_1_25_1","volume-title":"Proc. NIPS","author":"Williams O.","year":"2010","unstructured":"O. Williams and F. McSherry . Probabilistic inference and differential privacy . Proc. NIPS , 2010 . O. Williams and F. McSherry. Probabilistic inference and differential privacy. Proc. NIPS, 2010."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/2732296.2732300","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:17:37Z","timestamp":1672219057000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/2732296.2732300"}},"subtitle":["a platform for differentially-private analysis of weighted datasets"],"short-title":[],"issued":{"date-parts":[[2014,4]]},"references-count":25,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2014,4]]}},"alternative-id":["10.14778\/2732296.2732300"],"URL":"https:\/\/doi.org\/10.14778\/2732296.2732300","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2014,4]]}}}