差分隐私 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "差分隐私" in Chinese language version.

refsWebsite

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11web.archive.org

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10doi.org

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5microsoft.com

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3arxiv.org

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2wikipedia.org

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1springer.com

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1journalprivacyconfidentiality.org

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1cornell.edu

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1hu-berlin.de

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1eatcs.org

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1semanticscholar.org

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1utexas.edu

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1nature.com

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1upenn.edu

702^nd place

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1jstor.org

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1nih.gov

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1acm.org

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1github.com

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1googleblog.com

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1apple.com

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1privitar.com

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1sciencedirect.com

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acm.org

dl.acm.org

Úlfar Erlingsson, Vasyl Pihur, Aleksandra Korolova. "RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response". In Proceedings of the 21st ACM Conference on Computer and Communications Security (CCS), 2014. doi:10.1145/2660267.2660348

apple.com

Apple - Press Info - Apple Previews iOS 10, the Biggest iOS Release Ever. Apple. [16 June 2016]. （原始内容存档于2016-06-15）.

arxiv.org

Christos Dimitrakakis, Blaine Nelson, Aikaterini Mitrokotsa, Benjamin Rubinstein. Robust and Private Bayesian Inference. Algorithmic Learning Theory 2014. [2021-08-11]. （原始内容存档于2021-01-13）.
SoK: Differential Privacies by Damien Desfontaines, Balázs Pejó. 2019.
LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale by Ryan Rogers, Subbu Subramaniam, Sean Peng, David Durfee, Seunghyun Lee, Santosh Kumar Kancha, Shraddha Sahay, Parvez Ahammad. arXiv:2002.05839.

cornell.edu

archives.vrdc.cornell.edu

Dalenius, Tore. Towards a methodology for statistical disclosure control (PDF). Statistik Tidskrift. 1977, 15 [2021-08-10]. （原始内容存档 (PDF)于2021-07-18）.

doi.org

Calibrating Noise to Sensitivity in Private Data Analysis by Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith. In Theory of Cryptography Conference (TCC), Springer, 2006. doi:10.1007/11681878_14. The full version appears in Journal of Privacy and Confidentiality, 7 (3), 17-51. doi:10.29012/jpc.v7i3.405
Irit Dinur and Kobbi Nissim. 2003. Revealing information while preserving privacy. In Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (PODS '03). ACM, New York, NY, USA, 202–210. doi:10.1145/773153.773173
Dwork, Cynthia. Differential Privacy: A Survey of Results. Agrawal, Manindra; Du, Dingzhu; Duan, Zhenhua; Li, Angsheng (编). Theory and Applications of Models of Computation. Lecture Notes in Computer Science 4978. Springer Berlin Heidelberg. 2008-04-25: 1–19 [2021-08-10]. ISBN 9783540792277. doi:10.1007/978-3-540-79228-4_1. （原始内容存档于2021-02-27）（英语）.
de Montjoye, Yves-Alexandre; César A. Hidalgo; Michel Verleysen; Vincent D. Blondel. Unique in the Crowd: The privacy bounds of human mobility. Nature srep. March 25, 2013 [12 April 2013]. doi:10.1038/srep01376. （原始内容存档于2015-08-11）.
The Algorithmic Foundations of Differential Privacy by Cynthia Dwork and Aaron Roth. Foundations and Trends in Theoretical Computer Science. Vol. 9, no. 3–4, pp. 211‐407, Aug. 2014. doi:10.1561/0400000042
Privacy integrated queries: an extensible platform for privacy-preserving data analysis by Frank D. McSherry. In Proceedings of the 35th SIGMOD International Conference on Management of Data (SIGMOD), 2009. doi:10.1145/1559845.1559850
Differential Privacy by Cynthia Dwork, International Colloquium on Automata, Languages and Programming (ICALP) 2006, p. 1–12. doi:10.1007/11787006 1
Warner, S. L. Randomised response: a survey technique for eliminating evasive answer bias. Journal of the American Statistical Association（英语：Journal of the American Statistical Association） (Taylor & Francis). March 1965, 60 (309): 63–69. JSTOR 2283137. PMID 12261830. doi:10.1080/01621459.1965.10480775.
Úlfar Erlingsson, Vasyl Pihur, Aleksandra Korolova. "RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response". In Proceedings of the 21st ACM Conference on Computer and Communications Security (CCS), 2014. doi:10.1145/2660267.2660348
Fletcher, Sam; Islam, Md Zahidul. Differentially private random decision forests using smooth sensitivity. Expert Systems with Applications. July 2017, 78: 16–31. doi:10.1016/j.eswa.2017.01.034.

eatcs.org

2017 Gödel Prize. [2021-08-10]. （原始内容存档于2019-04-16）.

github.com

google/rappor, GitHub, 2021-07-15 [2017-09-01], （原始内容存档于2021-01-14）

googleblog.com

europe.googleblog.com

Tackling Urban Mobility with Technology by Andrew Eland. Google Policy Europe Blog, Nov 18, 2015.

hu-berlin.de

dbis.informatik.hu-berlin.de

Dorothy E. Denning; Peter J. Denning; Mayer D. Schwartz. The Tracker: A Threat to Statistical Database Security (PDF) 4 (1): 76–96. March 1978 [2021-08-10]. （原始内容存档 (PDF)于2021-08-21）.

journalprivacyconfidentiality.org

Calibrating Noise to Sensitivity in Private Data Analysis by Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith. In Theory of Cryptography Conference (TCC), Springer, 2006. doi:10.1007/11681878_14. The full version appears in Journal of Privacy and Confidentiality, 7 (3), 17-51. doi:10.29012/jpc.v7i3.405

jstor.org

Warner, S. L. Randomised response: a survey technique for eliminating evasive answer bias. Journal of the American Statistical Association（英语：Journal of the American Statistical Association） (Taylor & Francis). March 1965, 60 (309): 63–69. JSTOR 2283137. PMID 12261830. doi:10.1080/01621459.1965.10480775.

microsoft.com

research.microsoft.com

Privacy integrated queries: an extensible platform for privacy-preserving data analysis by Frank D. McSherry. In Proceedings of the 35th SIGMOD International Conference on Management of Data (SIGMOD), 2009. doi:10.1145/1559845.1559850
Differential Privacy by Cynthia Dwork, International Colloquium on Automata, Languages and Programming (ICALP) 2006, p. 1–12. doi:10.1007/11787006 1
F.McSherry and K.Talwar. Mechasim Design via Differential Privacy. Proceedings of the 48th Annual Symposium of Foundations of Computer Science, 2007. (PDF). [2021-08-11]. （原始内容存档 (PDF)于2016-03-07）.

microsoft.com

Dwork, Cynthia. Differential Privacy: A Survey of Results. Agrawal, Manindra; Du, Dingzhu; Duan, Zhenhua; Li, Angsheng (编). Theory and Applications of Models of Computation. Lecture Notes in Computer Science 4978. Springer Berlin Heidelberg. 2008-04-25: 1–19 [2021-08-10]. ISBN 9783540792277. doi:10.1007/978-3-540-79228-4_1. （原始内容存档于2021-02-27）（英语）.
Collecting telemetry data privately by Bolin Ding, Jana Kulkarni, Sergey Yekhanin. NIPS 2017.

nature.com

de Montjoye, Yves-Alexandre; César A. Hidalgo; Michel Verleysen; Vincent D. Blondel. Unique in the Crowd: The privacy bounds of human mobility. Nature srep. March 25, 2013 [12 April 2013]. doi:10.1038/srep01376. （原始内容存档于2015-08-11）.

nih.gov

ncbi.nlm.nih.gov

Warner, S. L. Randomised response: a survey technique for eliminating evasive answer bias. Journal of the American Statistical Association（英语：Journal of the American Statistical Association） (Taylor & Francis). March 1965, 60 (309): 63–69. JSTOR 2283137. PMID 12261830. doi:10.1080/01621459.1965.10480775.

privitar.com

Privitar Lens. [20 February 2018]. （原始内容存档于2019-04-05）.

sciencedirect.com

Fletcher, Sam; Islam, Md Zahidul. Differentially private random decision forests using smooth sensitivity. Expert Systems with Applications. July 2017, 78: 16–31. doi:10.1016/j.eswa.2017.01.034.

semanticscholar.org

api.semanticscholar.org

Hilton, Michael. Differential Privacy: A Historical Survey. S2CID 16861132.

springer.com

link.springer.com

Calibrating Noise to Sensitivity in Private Data Analysis by Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith. In Theory of Cryptography Conference (TCC), Springer, 2006. doi:10.1007/11681878_14. The full version appears in Journal of Privacy and Confidentiality, 7 (3), 17-51. doi:10.29012/jpc.v7i3.405

upenn.edu

cis.upenn.edu

The Algorithmic Foundations of Differential Privacy by Cynthia Dwork and Aaron Roth. Foundations and Trends in Theoretical Computer Science. Vol. 9, no. 3–4, pp. 211‐407, Aug. 2014. doi:10.1561/0400000042

utexas.edu

cs.utexas.edu

Arvind Narayanan（英语：Arvind Narayanan）, Vitaly Shmatikov（英语：Vitaly Shmatikov）. Robust De-anonymization of Large Sparse Datasets (PDF). IEEE Symposium on Security and Privacy（英语：IEEE Symposium on Security and Privacy）: 111–125. 2008 [2017-09-01]. （原始内容存档 (PDF)于2021-01-26）.

web.archive.org

Dalenius, Tore. Towards a methodology for statistical disclosure control (PDF). Statistik Tidskrift. 1977, 15 [2021-08-10]. （原始内容存档 (PDF)于2021-07-18）.
Dorothy E. Denning; Peter J. Denning; Mayer D. Schwartz. The Tracker: A Threat to Statistical Database Security (PDF) 4 (1): 76–96. March 1978 [2021-08-10]. （原始内容存档 (PDF)于2021-08-21）.
2017 Gödel Prize. [2021-08-10]. （原始内容存档于2019-04-16）.
Dwork, Cynthia. Differential Privacy: A Survey of Results. Agrawal, Manindra; Du, Dingzhu; Duan, Zhenhua; Li, Angsheng (编). Theory and Applications of Models of Computation. Lecture Notes in Computer Science 4978. Springer Berlin Heidelberg. 2008-04-25: 1–19 [2021-08-10]. ISBN 9783540792277. doi:10.1007/978-3-540-79228-4_1. （原始内容存档于2021-02-27）（英语）.
Arvind Narayanan（英语：Arvind Narayanan）, Vitaly Shmatikov（英语：Vitaly Shmatikov）. Robust De-anonymization of Large Sparse Datasets (PDF). IEEE Symposium on Security and Privacy（英语：IEEE Symposium on Security and Privacy）: 111–125. 2008 [2017-09-01]. （原始内容存档 (PDF)于2021-01-26）.
de Montjoye, Yves-Alexandre; César A. Hidalgo; Michel Verleysen; Vincent D. Blondel. Unique in the Crowd: The privacy bounds of human mobility. Nature srep. March 25, 2013 [12 April 2013]. doi:10.1038/srep01376. （原始内容存档于2015-08-11）.
F.McSherry and K.Talwar. Mechasim Design via Differential Privacy. Proceedings of the 48th Annual Symposium of Foundations of Computer Science, 2007. (PDF). [2021-08-11]. （原始内容存档 (PDF)于2016-03-07）.
Christos Dimitrakakis, Blaine Nelson, Aikaterini Mitrokotsa, Benjamin Rubinstein. Robust and Private Bayesian Inference. Algorithmic Learning Theory 2014. [2021-08-11]. （原始内容存档于2021-01-13）.
google/rappor, GitHub, 2021-07-15 [2017-09-01], （原始内容存档于2021-01-14）
Apple - Press Info - Apple Previews iOS 10, the Biggest iOS Release Ever. Apple. [16 June 2016]. （原始内容存档于2016-06-15）.
Privitar Lens. [20 February 2018]. （原始内容存档于2019-04-05）.

wikipedia.org

en.wikipedia.org

Arvind Narayanan（英语：Arvind Narayanan）, Vitaly Shmatikov（英语：Vitaly Shmatikov）. Robust De-anonymization of Large Sparse Datasets (PDF). IEEE Symposium on Security and Privacy（英语：IEEE Symposium on Security and Privacy）: 111–125. 2008 [2017-09-01]. （原始内容存档 (PDF)于2021-01-26）.
Warner, S. L. Randomised response: a survey technique for eliminating evasive answer bias. Journal of the American Statistical Association（英语：Journal of the American Statistical Association） (Taylor & Francis). March 1965, 60 (309): 63–69. JSTOR 2283137. PMID 12261830. doi:10.1080/01621459.1965.10480775.