{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T07:14:38Z","timestamp":1763968478540,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T00:00:00Z","timestamp":1640044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"United States National Science Foundation","award":["1931443"],"award-info":[{"award-number":["1931443"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,21]]},"DOI":"10.1145\/3491371.3491385","type":"proceedings-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T12:35:27Z","timestamp":1638448527000},"page":"106-119","source":"Crossref","is-referenced-by-count":3,"title":["MGD: A Utility Metric for Private Data Publication"],"prefix":"10.1145","author":[{"given":"Zitao","family":"Li","sequence":"first","affiliation":[{"name":"Purdue University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trung","family":"Dang","sequence":"additional","affiliation":[{"name":"Purdue University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianhao","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ninghui","family":"Li","sequence":"additional","affiliation":[{"name":"Purdue University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,12,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3226070"},{"volume-title":"Network Flows: Theory, Algorithms, and Applications. Prentice hall.","year":"1993","author":"Ahuja K","key":"e_1_3_2_1_3_1"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305404"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Avrim Blum Katrina Ligett and Aaron Roth. 2008. A learning theory approach to non-interactive database privacy. In STOC. 609\u2013618. Avrim Blum Katrina Ligett and Aaron Roth. 2008. A learning theory approach to non-interactive database privacy. In STOC. 609\u2013618.","DOI":"10.1145\/1374376.1374464"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476272"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.1999.790393"},{"key":"e_1_3_2_1_8_1","unstructured":"Bolin Ding Janardhan Kulkarni and Sergey Yekhanin. 2017. Collecting Telemetry Data Privately. In Advances in Neural Information Processing Systems. 3574\u20133583. Bolin Ding Janardhan Kulkarni and Sergey Yekhanin. 2017. Collecting Telemetry Data Privately. In Advances in Neural Information Processing Systems. 3574\u20133583."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Cynthia Dwork Frank McSherry Kobbi Nissim and Adam Smith. 2006. Calibrating Noise to Sensitivity in Private Data Analysis. In TCC. 265\u2013284. Cynthia Dwork Frank McSherry Kobbi Nissim and Adam Smith. 2006. Calibrating Noise to Sensitivity in Private Data Analysis. In TCC. 265\u2013284.","DOI":"10.1007\/11681878_14"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660348"},{"key":"e_1_3_2_1_11_1","unstructured":"Facebook. [n.d.]. Opacus. https:\/\/opacus.ai\/. Facebook. [n.d.]. Opacus. https:\/\/opacus.ai\/."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2016-0015"},{"volume-title":"International Conference on Machine Learning. 1170\u20131178","year":"2014","author":"Gaboardi Marco","key":"e_1_3_2_1_13_1"},{"key":"e_1_3_2_1_14_1","unstructured":"Google. [n.d.]. TensorFlow Privacy. https:\/\/github.com\/tensorflow\/privacy. Google. [n.d.]. TensorFlow Privacy. https:\/\/github.com\/tensorflow\/privacy."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2004.1315035"},{"key":"e_1_3_2_1_16_1","unstructured":"Ishaan Gulrajani Faruk Ahmed Martin Arjovsky Vincent Dumoulin and Aaron\u00a0C Courville. 2017. Improved training of wasserstein gans. In Advances in neural information processing systems. 5767\u20135777. Ishaan Gulrajani Faruk Ahmed Martin Arjovsky Vincent Dumoulin and Aaron\u00a0C Courville. 2017. Improved training of wasserstein gans. In Advances in neural information processing systems. 5767\u20135777."},{"key":"e_1_3_2_1_17_1","unstructured":"Moritz Hardt Katrina Ligett and Frank McSherry. 2012. A simple and practical algorithm for differentially private data release. In Advances in Neural Information Processing Systems. 2339\u20132347. Moritz Hardt Katrina Ligett and Frank McSherry. 2012. A simple and practical algorithm for differentially private data release. In Advances in Neural Information Processing Systems. 2339\u20132347."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3187009.3177733"},{"volume-title":"International Conference on Learning Representations.","year":"2018","author":"Karras Tero","key":"e_1_3_2_1_19_1"},{"volume-title":"International conference on machine learning. 957\u2013966","year":"2015","author":"Kusner Matt","key":"e_1_3_2_1_20_1"},{"volume-title":"California Consumer Privacy Act of","year":"2018","author":"Legislature California\u00a0State","key":"e_1_3_2_1_21_1"},{"volume-title":"An efficient earth mover\u2019s distance algorithm for robust histogram comparison","year":"2007","author":"Ling Haibin","key":"e_1_3_2_1_22_1"},{"volume-title":"Relaxed Marginal Consistency for Differentially Private Query Answering. arXiv","year":"2021","author":"McKenna Ryan","key":"e_1_3_2_1_23_1"},{"volume-title":"International Conference on Machine Learning. PMLR, 4435\u20134444","year":"2019","author":"McKenna Ryan","key":"e_1_3_2_1_24_1"},{"key":"e_1_3_2_1_25_1","unstructured":"NIST. [n.d.]. 2018 Differential Privacy Synthetic Data Challenge. https:\/\/www.nist.gov\/ctl\/pscr\/open-innovation-prize-challenges\/past-prize-challenges\/2018-differential-privacy-synthetic. NIST. [n.d.]. 2018 Differential Privacy Synthetic Data Challenge. https:\/\/www.nist.gov\/ctl\/pscr\/open-innovation-prize-challenges\/past-prize-challenges\/2018-differential-privacy-synthetic."},{"key":"e_1_3_2_1_26_1","unstructured":"NIST. [n.d.]. DeID2 - A Better Meter Stick for Differential Privacy. https:\/\/www.herox.com\/bettermeterstick\/teams. NIST. [n.d.]. DeID2 - A Better Meter Stick for Differential Privacy. https:\/\/www.herox.com\/bettermeterstick\/teams."},{"key":"e_1_3_2_1_27_1","unstructured":"NIST. [n.d.]. Differential Privacy Temporal Map Challenge: Sprint 1. https:\/\/www.drivendata.org\/competitions\/69\/deid2-sprint-1-prescreened\/page\/263\/. NIST. [n.d.]. Differential Privacy Temporal Map Challenge: Sprint 1. https:\/\/www.drivendata.org\/competitions\/69\/deid2-sprint-1-prescreened\/page\/263\/."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Ryan Rogers Subbu Subramaniam Sean Peng David Durfee Seunghyun Lee Santosh\u00a0Kumar Kancha Shraddha Sahay and Parvez Ahammad. 2020. LinkedIn\u2019s Audience Engagements API: A privacy preserving data analytics system at scale. arXiv preprint arXiv:2002.05839(2020). Ryan Rogers Subbu Subramaniam Sean Peng David Durfee Seunghyun Lee Santosh\u00a0Kumar Kancha Shraddha Sahay and Parvez Ahammad. 2020. LinkedIn\u2019s Audience Engagements API: A privacy preserving data analytics system at scale. arXiv preprint arXiv:2002.05839(2020).","DOI":"10.29012\/jpc.782"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/3152676"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33718-5_32"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064047"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3134428"},{"volume-title":"Privsyn: Differentially private data synthesis. In 30th {USENIX} Security Symposium ({USENIX} Security 21).","year":"2021","author":"Zhang Zhikun","key":"e_1_3_2_1_33_1"}],"event":{"name":"8th NSysS 2021: 8th International Conference on Networking, Systems and Security","acronym":"8th NSysS 2021","location":"Cox's Bazar Bangladesh"},"container-title":["8th International Conference on Networking, Systems and Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3491371.3491385","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3491371.3491385","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:21Z","timestamp":1750183761000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3491371.3491385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,21]]},"references-count":33,"alternative-id":["10.1145\/3491371.3491385","10.1145\/3491371"],"URL":"https:\/\/doi.org\/10.1145\/3491371.3491385","relation":{},"subject":[],"published":{"date-parts":[[2021,12,21]]}}}