35:
1197:
Aktay, Ahmet; Bavadekar, Shailesh; Cossoul, Gwen; Davis, John; Desfontaines, Damien; Fabrikant, Alex; Gabrilovich, Evgeniy; Gadepalli, Krishna; Gipson, Bryant; Guevara, Miguel; Kamath, Chaitanya; Kansal, Mansi; Lange, Ali; Mandayam, Chinmoy; Oplinger, Andrew; Pluntke, Christopher; Roessler, Thomas;
214:
Emoji analytics; analytics. Improve: QuickType, emoji; Spotlight deep link suggestions; Lookup Hints in Notes. Emoji suggestions, health type usage estimates, Safari energy drain statistics, Autoplay intent detection (also in Safari)
1219:
Rogers, Ryan; Subbu, Subramaniam; Peng, Sean; Durfee, David; Lee, Seunghyun; Kancha, Santosh Kumar; Sahay, Shraddha; Ahammad, Parvez (2020). "LinkedIn's
Audience Engagements API: A Privacy Preserving Data Analytics System at Scale".
1153:
Rogers, Ryan; Subramaniam, Subbu; Peng, Sean; Durfee, David; Lee, Seunghyun; Santosh Kumar Kancha; Sahay, Shraddha; Ahammad, Parvez (2020). "LinkedIn's
Audience Engagements API: A Privacy Preserving Data Analytics System at Scale".
1198:
Schlosberg, Arran; Shekel, Tomer; Vispute, Swapnil; Vu, Mia; Wellenius, Gregory; Williams, Brian; Royce J Wilson (2020). "Google COVID-19 Community
Mobility Reports: Anonymization Process Description (Version 1.1)".
1175:
Rogers, Ryan; Adrian Rivera
Cardoso; Mancuhan, Koray; Kaura, Akash; Gahlawat, Nikhil; Jain, Neha; Ko, Paul; Ahammad, Parvez (2020). "A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale".
414:
These software packages purport to be usable in production systems. They are split in two categories: those focused on answering statistical queries with differential privacy, and those focused on training
147:, a number of systems supporting differentially private data analyses have been implemented and deployed. This article tracks real-world deployments, production software packages, and research prototypes.
1768:
Zhang, Dan; McKenna, Ryan; Kotsogiannis, Ios; Hay, Michael; Machanavajjhala, Ashwin; Miklau, Gerome (June 2018). "EKTELO: A Framework for
Defining Differentially-Private Computations".
1241:
Pereira, Mayana; Kim, Allen; Allen, Joshua; White, Kevin; Juan
Lavista Ferres; Dodhia, Rahul (2021). "U.S. Broadband Coverage Data Set: A Differentially Private Data Release".
111:
783:
83:
90:
1595:
751:
97:
79:
853:
Machanavajjhala, Ashwin; Kifer, Daniel; Abowd, John; Gehrke, Johannes; Vilhuber, Lars (April 2008). "Privacy: Theory meets
Practice on the Map".
909:
Erlingsson, Úlfar; Pihur, Vasyl; Korolova, Aleksandra (November 2014). "RAPPOR: Randomized
Aggregatable Privacy-Preserving Ordinal Response".
59:
of the topic and provide significant coverage of it beyond a mere trivial mention. If notability cannot be shown, the article is likely to be
104:
1573:
Holohan, Naoise; Braghin, Stefano; Pól Mac
Aonghusa; Levacher, Killian (2019). "Diffprivlib: The IBM Differential Privacy Library".
870:
1047:
1518:
Gaboardi, Marco; Honaker, James; King, Gary; Nissim, Kobbi; Ullman, Jonathan; Vadhan, Salil; Murtagh, Jack (June 2016).
895:
1795:
1655:
1129:
1061:
Johnson, Noah; Near, Joseph P.; Song, Dawn (January 2018). "Towards
Practical Differential Privacy for SQL Queries".
946:
130:
971:
1287:
Hod, Shlomi; Canetti, Ran (2024). "Differentially Private Release of Israel's National Registry of Live Births".
809:
1824:
56:
45:
1829:
1533:
1353:
1013:
Ding, Bolin; Kulkarni, Janardhan; Yekhanin, Sergey (December 2017). "Collecting Telemetry Data Privately".
576:
204:
52:
1697:
585:
68:
1104:
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
1718:
Haeberlen, Andreas; Pierce, Benjamin C.; Narayan, Arjun (2011). "Differential Privacy Under Fire".
482:
1696:
Roy, Indrajit; Setty, Srinath T.V.; Kilzer, Ann; Shmatikov, Vitaly; Witchel, Emmett (April 2010).
23:
768:
1519:
999:
1705:
Proceedings of the 7th Usenix Symposium on Networked Systems Design and Implementation (NSDI)
987:
19:
1028:
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804:
144:
8:
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916:
876:
557:
64:
1596:"Introducing TensorFlow Privacy: Learning with Differential Privacy for Training Data"
1553:
1791:
1125:
942:
912:
Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security
866:
60:
1682:
1139:
1805:
1783:
1754:
1742:
1737:; Song, Dawn; Culler, David E. "GUPT: Privacy Preserving Data Analysis Made Easy".
1670:
1115:
1107:
1080:
956:
934:
880:
858:
416:
1739:
Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
1333:
1099:
738:
Fuzz: Time-constant implementation in Caml Light of a domain-specific language.
667:
910:
862:
177:
OnTheMap: Interactive tool for exploration of US income and commute patterns.
1818:
726:-based system implemented in Java hardened with SELinux-like access control.
1787:
1746:
1674:
1354:"Differential-privacy/Privacy-on-beam at main · google/Differential-privacy"
1111:
1084:
938:
458:
Building block libraries in Go, C++, and Java; end-to-end framework in Go,.
527:
503:
1308:
531:
1609:
1174:
1734:
653:
1770:
Proceedings of the 2018 International Conference on Management of Data
1572:
1120:
785:
KTELO: A framework and system for answering linear counting queries.
723:
473:
1778:
1579:
1398:
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1226:
1204:
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828:
357:
921:
676:
469:
51:
Please help to demonstrate the notability of the topic by citing
1614:
1558:
1538:
1403:
1358:
1338:
896:"Learning statistics with privacy, aided by the flip of a coin"
852:
644:
517:
450:
248:
Flex: A SQL-based system developed for internal Uber analytics
1493:
390:
1048:"Uber Releases Open Source Project for Differential Privacy"
80:"List of implementations of differentially private analyses"
1767:
1262:
1152:
855:
2008 IEEE 24th International Conference on Data Engineering
1373:
1196:
1630:"Opacus · Train PyTorch models with Differential Privacy"
1517:
1468:
969:
625:
1418:
1015:
31st Conference on Neural Information Processing Systems
1695:
1443:
908:
1717:
1629:
1594:
Radebaugh, Carey; Erlingsson, Ulfar (March 6, 2019).
1218:
771:
194:
RAPPOR in Chrome Browser to collect security metrics
1240:
1100:"The U.S. Census Bureau Adopts Differential Privacy"
1012:
1593:
688:
777:
1444:"Tumult Labs | Privacy Protection Redefined"
1816:
1732:
1534:"DAS 2020 Redistricting Production Code Release"
240:Application usage statistics Microsoft Windows.
1060:
409:
18:"Pinq" redirects here. Not to be confused with
1711:
1698:"Airavat: Security and Privacy for MapReduce"
1520:"PSI (Ψ): a Private data Sharing Interface"
970:Differential Privacy Team (December 2017).
1286:
1045:
46:notability guideline for stand-alone lists
1777:
1578:
1334:"Google's differential privacy libraries"
1292:
1246:
1225:
1203:
1181:
1159:
1119:
1074:
1022:
920:
186:First deployment of differential privacy
150:
131:Learn how and when to remove this message
1653:
447:Google's differential privacy libraries
1733:Mohan, Prashanth; Thakurta, Abhradeep;
422:
1817:
893:
1097:
1654:McSherry, Frank (1 September 2010).
312:COVID-19 Community Mobility Reports
28:
675:Differentially private training in
652:Differentially private training in
597:
13:
1046:Tezapsidis, Katie (Jul 13, 2017).
419:models with differential privacy.
14:
1841:
1063:Proceedings of the VLDB Endowment
485:in Python with an SQL interface.
341:U.S. Broadband Coverage Data Set
972:"Learning with Privacy at Scale"
710:PINQ: An API implemented in C#.
689:Research projects and prototypes
33:
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1720:20th USENIX Security Symposium
1098:Abowd, John M. (August 2018).
1091:
1054:
1039:
1006:
976:Apple Machine Learning Journal
963:
902:
887:
846:
821:
810:Secure multi-party computation
1:
1309:"Live Birth Dataset (Hebrew)"
815:
1656:"Privacy integrated queries"
750:GUPT: Implementation of the
584:Production code used in the
410:Production software packages
7:
798:
577:United States Census Bureau
526:Python library, running on
502:Python library, running on
395:Israeli Ministry of Health
362:IRS and Dept. of Education
10:
1846:
205:local differential privacy
53:reliable secondary sources
42:The topic of this article
17:
1663:Communications of the ACM
863:10.1109/ICDE.2008.4497436
778:{\displaystyle \epsilon }
358:College Scorecard Website
44:may not meet Knowledge's
280:Audience Engagement API
203:First widespread use of
1788:10.1145/3183713.3196921
1747:10.1145/2213836.2213876
1675:10.1145/1810891.1810916
1112:10.1145/3219819.3226070
1085:10.1145/3187009.3177733
939:10.1145/2660267.2660348
833:onthemap.ces.census.gov
560:Privacy Tools Project.
915:. pp. 1054–1067.
779:
481:Core library in Rust,
296:Labor Market Insights
231:Application telemetry
151:Real-world deployments
780:
1825:Differential privacy
1772:. pp. 115–130.
1741:. pp. 349–360.
1610:"TensorFlow Privacy"
857:. pp. 277–286.
805:Differential Privacy
769:
752:sample-and-aggregate
423:Statistical analyses
145:differential privacy
143:Since the advent of
1830:Information privacy
1033:2017arXiv171201524D
931:2014arXiv1407.6981E
894:Erlingsson, Úlfar.
641:TensorFlow Privacy
328:Advertiser Queries
1562:. 17 October 2022.
1554:"Diffprivlib v0.5"
1419:"Tumult Analytics"
1342:. 3 February 2023.
998:has generic name (
775:
617:Still maintained?
573:TopDown Algorithm
558:Harvard University
442:Still maintained?
391:Live Birth Dataset
48:
1498:www.openmined.org
872:978-1-4244-1836-7
796:
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686:
685:
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493:Tumult Analytics
407:
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267:US Census Bureau
180:US Census Bureau
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1106:. p. 2867.
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633:Python library.
611:Year Introduced
602:
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598:Machine learning
436:Year Introduced
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417:machine learning
166:Year Introduced
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1542:. 22 June 2022.
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1618:. 2019-08-09.
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532:Apache Beam
514:PipelineDP
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1779:1808.03555
1580:1907.02444
1378:opendp.org
1294:2405.00267
1248:2103.14035
1227:2002.05839
1205:2004.04145
1183:2010.13981
1161:2002.05839
1121:1813/60392
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1024:1712.01524
829:"OnTheMap"
816:References
654:TensorFlow
608:Developer
433:Developer
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1634:opacus.ai
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1683:52898716
1639:29 March
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1374:"OpenDP"
1272:29 March
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