差分隐私 - 版本历史

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历史

薄荷：/* 现实世界中差分隐私算法的应用 */

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现实世界中差分隐私算法的应用

2021年12月19日 (日) 03:46 薄荷

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2021年12月19日 (日) 03:39 薄荷

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 ==编者推荐==
-[[File:差分隐私.png|400px|[https://campus.swarma.org/course/2812 本地化差分隐私下的数据分析技术探索 ]]]
+[[File:差分隐私.png|thumb|right|400px|[https://campus.swarma.org/course/2812 本地化差分隐私下的数据分析技术探索 ]]]
 ===集智课程===
 ====[https://campus.swarma.org/course/2812 本地化差分隐私下的数据分析技术探索 ]====

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 ==编者推荐==
 ===集智课程===
-====[]====
+====[https://campus.swarma.org/course/2812 本地化差分隐私下的数据分析技术探索 ]====

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 *Erlingsson, Úlfar, Vasyl Pihur and Aleksandra Korolova. 2014. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (CCS '14). ACM, New York, NY, USA, 1054-1067. {{doi|10.1145/2660267.2660348}}.
 *Abowd, John M. and Ian M. Schmutte. 2017 . Revisiting the economics of privacy: Population statistics and confidentiality protection as public goods. Labor Dynamics Institute, Cornell University, Labor Dynamics Institute, Cornell University, at https://digitalcommons.ilr.cornell.edu/ldi/37/
-*Abowd, John M. and Ian M. Schmutte. Forthcoming. An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices. American Economic Review,  {{arxiv|1808.06303}}
+*Abowd, John M. and Ian M. Schmutte. Forthcoming. An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices. American Economic Review.
 *Apple, Inc. 2016. Apple previews iOS 10, the biggest iOS release ever. Press Release (June 13). https://www.apple.com/newsroom/2016/06/apple-previews-ios-10-biggest-ios-release-ever.html.
 *Ding, Bolin, Janardhan Kulkarni, and Sergey Yekhanin 2017. Collecting Telemetry Data Privately, NIPS 2017.

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 * Dinur, Irit 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, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. in Halevi, S. & Rabin, T. (Eds.) Calibrating Noise to Sensitivity in Private Data Analysis Theory of Cryptography: Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, March 4–7, 2006. Proceedings, Springer Berlin Heidelberg, 265-284, {{doi|10.1007/11681878 14}}.
-*Dwork, Cynthia. 2006. Differential Privacy, 33rd International Colloquium on Automata, Languages and Programming, part II (ICALP 2006), Springer Verlag, 4052, 1-12, {{ISBN|3-540-35907-9}}.
+*Dwork, Cynthia. 2006. Differential Privacy, 33rd International Colloquium on Automata, Languages and Programming, part II (ICALP 2006), Springer Verlag, 4052, 1-12,\.
 *Dwork, Cynthia and Aaron Roth. 2014. The Algorithmic Foundations of Differential Privacy. Foundations and Trends in Theoretical Computer Science. Vol. 9, Nos. 3–4. 211–407, {{doi|10.1561/0400000042}}.
 *Machanavajjhala, Ashwin, Daniel Kifer, John M. Abowd, Johannes Gehrke, and Lars Vilhuber. 2008. Privacy: Theory Meets Practice on the Map, International Conference on Data Engineering (ICDE) 2008: 277-286, {{doi|10.1109/ICDE.2008.4497436}}.
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 *Ryffel, Theo, Andrew Trask, et. al. [[arxiv:1811.04017|"A generic framework for privacy preserving deep learning"]]
 ==相关链接==

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−	自此，后续的研究展示了许多方法，可以在保证高度隐私的同时，从数据库中生成非常准确的统计数据。<ref name=":5">{{Cite journal\|last=Hilton\|first=Michael~~\|s2cid=16861132~~\|title=Differential Privacy: A Historical Survey}}</ref><ref name=":6">{{Cite book\|title=Theory and Applications of Models of Computation\|volume=4978\|last=Dwork\|first=Cynthia\|date=2008-04-25\|publisher=Springer Berlin Heidelberg\|isbn=9783540792277\|editor-last=Agrawal\|editor-first=Manindra\|series=Lecture Notes in Computer Science\|pages=1–19\|language=en\|chapter=Differential Privacy: A Survey of Results\|doi=10.1007/978-3-540-79228-4_1\|editor-last2=Du\|editor-first2=Dingzhu\|editor-last3=Duan\|editor-first3=Zhenhua\|editor-last4=Li\|editor-first4=Angsheng\|chapter-url=https://www.microsoft.com/en-us/research/publication/differential-privacy-a-survey-of-results/}}</ref>	+	自此，后续的研究展示了许多方法，可以在保证高度隐私的同时，从数据库中生成非常准确的统计数据。<ref name=":5">{{Cite journal\|last=Hilton\|first=Michael\|title=Differential Privacy: A Historical Survey}}</ref><ref name=":6">{{Cite book\|title=Theory and Applications of Models of Computation\|volume=4978\|last=Dwork\|first=Cynthia\|date=2008-04-25\|publisher=Springer Berlin Heidelberg\|isbn=9783540792277\|editor-last=Agrawal\|editor-first=Manindra\|series=Lecture Notes in Computer Science\|pages=1–19\|language=en\|chapter=Differential Privacy: A Survey of Results\|doi=10.1007/978-3-540-79228-4_1\|editor-last2=Du\|editor-first2=Dingzhu\|editor-last3=Duan\|editor-first3=Zhenhua\|editor-last4=Li\|editor-first4=Angsheng\|chapter-url=https://www.microsoft.com/en-us/research/publication/differential-privacy-a-survey-of-results/}}</ref>

	</br>		</br>

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 *2017：微软，用于Windows遥测系统。.<ref name=":13">{{cite web|title=Apple - Press Info - Apple Previews iOS 10, the Biggest iOS Release Ever|url=https://www.apple.com/pr/library/2016/06/13Apple-Previews-iOS-10-The-Biggest-iOS-Release-Ever.html|website=Apple|access-date=16 June 2016}}</ref>
 *2019：Priveritar Lens是一个使用差分隐私技术的 API。<ref name=":14">{{cite web|title=Privitar Lens|url=https://www.privitar.com/privitar-lens|access-date=20 February 2018}}</ref>
-*2020：LinkedIn，用于广告商查询。<ref name="DpLinkedIn" />
+*2020：LinkedIn，用于广告商查询。<ref name="DpLinkedIn">[https://arxiv.org/abs/2002.05839 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.</ref>
 ==公共目标的考虑 ==

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 </center>
-其中概率取代了算法所使用的随机性，那么算法<math>\mathcal{A}</math>被认为实现了 <math>\epsilon</math>差分隐私。<ref name="DPBook" />
+其中概率取代了算法所使用的随机性，那么算法<math>\mathcal{A}</math>被认为实现了 <math>\epsilon</math>差分隐私。<ref name="DPBook">
@@ 第101行： / 第102行： @@
 ===敏感性===
-设 <math>d</math>为一正整数，<math>\mathcal{D}</math>为一数据集，<math>f \colon \mathcal{D} \rightarrow \mathbb{R}^d</math>是函数。函数的灵敏度<ref name="DMNS06" />记为<math>\Delta f</math>，定义为：
+设 <math>d</math>为一正整数，<math>\mathcal{D}</math>为一数据集，<math>f \colon \mathcal{D} \rightarrow \mathbb{R}^d</math>是函数。函数的灵敏度<ref name="DMNS06">
 <math>\Delta f=\max \lVert f(D_1)-f(D_2) \rVert_1,</math>
@@ 第119行： / 第121行： @@
-其最多为 <math>e^{\frac{|f(D_{1})-f(D_{2})|}{\lambda}}\leq e^{\frac{\Delta(f)}{\lambda}}\,\!</math>。我们可以设<math>\frac{\Delta(f)}{\lambda}\,\!</math>为隐私因子<math>\epsilon\,\!</math>。因此，<math>\mathcal{T}\,\!</math>满足差分隐私机制的规则（从[[#&epsilon;-differential privacy|上面的定义]]可以看出）。如果尝试在我们的糖尿病例子中使用这个概念，那么从上述事实可以推出，为了使用<math>\mathcal{A}\,\!</math>作为<math>\epsilon\,\!</math>-差分隐私算法，我们需要满足<math>\lambda=1/\epsilon\,\!</math>。虽然我们在这里使用了拉普拉斯噪音，但是其他形式的噪音，如高斯噪音也是可使用的——但是可能需要稍微放宽差分隐私的定义。<ref name="Dwork, ICALP 2006" />
+其最多为 <math>e^{\frac{|f(D_{1})-f(D_{2})|}{\lambda}}\leq e^{\frac{\Delta(f)}{\lambda}}\,\!</math>。我们可以设<math>\frac{\Delta(f)}{\lambda}\,\!</math>为隐私因子<math>\epsilon\,\!</math>。因此，<math>\mathcal{T}\,\!</math>满足差分隐私机制的规则（从[[#&epsilon;-differential privacy|上面的定义]]可以看出）。如果尝试在我们的糖尿病例子中使用这个概念，那么从上述事实可以推出，为了使用<math>\mathcal{A}\,\!</math>作为<math>\epsilon\,\!</math>-差分隐私算法，我们需要满足<math>\lambda=1/\epsilon\,\!</math>。虽然我们在这里使用了拉普拉斯噪音，但是其他形式的噪音，如高斯噪音也是可使用的——但是可能需要稍微放宽差分隐私的定义。<ref name="Dwork, ICALP 2006">
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 ===稳定转换===
-对于任意两个数据库<math>A,B</math>，如果<math>T(A)</math>和 <math>T(B)</math>)之间的汉明距离最多是<math>A</math>和<math>B</math>之间的汉明距离Hamming Distance的<math>c</math>倍，则变换<math>T</math>是 <math>c</math>-稳定的。文章<ref name="PINQ" />中的定理2指出，如果存在一个机制<math>M</math>是<math>\epsilon</math>-差分隐私的，那么复合机制<math>M\circ T</math>也是<math>(\epsilon \times c)</math>-差分隐私的。
+对于任意两个数据库<math>A,B</math>，如果<math>T(A)</math>和 <math>T(B)</math>)之间的汉明距离最多是<math>A</math>和<math>B</math>之间的汉明距离Hamming Distance的<math>c</math>倍，则变换<math>T</math>是 <math>c</math>-稳定的。文章<ref name="PINQ">
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 ==现实世界中差分隐私算法的应用==
 在实践中，差分隐私的几个用途已经为人所知：
-*2008：美国人口普查局，用于显示通勤模式。<ref name="MachanavajjhalaKAGV08" />
+*2008：美国人口普查局，用于显示通勤模式。<ref name="MachanavajjhalaKAGV08">
-*2014：谷歌的 RAPPOR，用于遥测，例如了解不受欢迎的软件劫持用户设置的统计数据<ref name="RAPPOR" /><ref name=":12">{{Citation|title=google/rappor|date=2021-07-15|url=https://github.com/google/rappor|publisher=GitHub}}</ref>
+Ashwin Machanavajjhala, Daniel Kifer, John M. Abowd, Johannes Gehrke, and Lars Vilhuber. "Privacy: Theory meets Practice on the Map". In Proceedings of the 24th International Conference on Data Engineering, ICDE) 2008.</ref>
-*2015：Google，用于分享历史流量统计数据。<ref name="Eland" />
+*2014：谷歌的 RAPPOR，用于遥测，例如了解不受欢迎的软件劫持用户设置的统计数据<ref name="RAPPOR">
 *2016：苹果公司宣布打算在iOS 10中使用差分隐私来改进其智能个人助理技术。<ref name=":13" />
 *2017：微软，用于Windows遥测系统。.<ref name=":13">{{cite web|title=Apple - Press Info - Apple Previews iOS 10, the Biggest iOS Release Ever|url=https://www.apple.com/pr/library/2016/06/13Apple-Previews-iOS-10-The-Biggest-iOS-Release-Ever.html|website=Apple|access-date=16 June 2016}}</ref>
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 ==参考文献==
-{{Reflist|refs=
+<references/>
 <br>

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 ==参考文献==
-<references/>
+{{Reflist|refs=
 <ref name="DKMMN06">
 Dwork, Cynthia, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor. "Our data, ourselves: Privacy via distributed noise generation." In Advances in Cryptology-EUROCRYPT 2006, pp. 486–503. Springer Berlin Heidelberg, 2006.</ref>
-<ref name="CABP13">
+<!-- unused refs  <ref name="CABP13">
 Chatzikokolakis, Konstantinos, Miguel E. Andrés, Nicolás Emilio Bordenabe, and Catuscia Palamidessi. "Broadening the scope of Differential Privacy using metrics." In Privacy Enhancing Technologies, pp. 82–102. Springer Berlin Heidelberg, 2013.</ref>
@@ 第228行： / 第227行： @@
 <ref name="RAPPOR">
-Úlfar Erlingsson, Vasyl Pihur, Aleksandra Korolova. [https://dl.acm.org/doi/10.1145/2660267.2660348 "RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response".] In Proceedings of the 21st ACM Conference on Computer and Communications Security (CCS), 2014. </ref>
+Úlfar Erlingsson, Vasyl Pihur, Aleksandra Korolova. [https://dl.acm.org/doi/10.1145/2660267.2660348 "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}}</ref>
 <ref name="DMNS06">
-[https://link.springer.com/chapter/10.1007%2F11681878_14 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 [https://journalprivacyconfidentiality.org/index.php/jpc/article/view/405 full version] appears in Journal of Privacy and Confidentiality, 7 (3), 17-51. </ref>
+[https://link.springer.com/chapter/10.1007%2F11681878_14 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 [https://journalprivacyconfidentiality.org/index.php/jpc/article/view/405 full version] appears in Journal of Privacy and Confidentiality, 7 (3), 17-51. {{doi|10.29012/jpc.v7i3.405}}</ref>
 <ref name="PINQ">
-[http://research.microsoft.com/pubs/80218/sigmod115-mcsherry.pdf 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. </ref>
+[http://research.microsoft.com/pubs/80218/sigmod115-mcsherry.pdf 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}}</ref>
 <ref name="Dwork, ICALP 2006">
-[http://research.microsoft.com/pubs/64346/dwork.pdf Differential Privacy] by Cynthia Dwork, International Colloquium on Automata, Languages and Programming (ICALP) 2006, p.&nbsp;1–12.</ref>
+[http://research.microsoft.com/pubs/64346/dwork.pdf Differential Privacy] by Cynthia Dwork, International Colloquium on Automata, Languages and Programming (ICALP) 2006, p.&nbsp;1–12. {{doi|10.1007/11787006 1}}</ref>
 <ref name="DPBook">
-[http://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf 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.&nbsp;211‐407, Aug. 2014.</ref>
+[http://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf 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.&nbsp;211‐407, Aug. 2014. {{doi|10.1561/0400000042}}</ref>
 <ref name="Eland">[https://europe.googleblog.com/2015/11/tackling-urban-mobility-with-technology.html Tackling Urban Mobility with Technology] by Andrew Eland. Google Policy Europe Blog, Nov 18, 2015.</ref>
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 <ref name="DpWinTelemetry">[https://www.microsoft.com/en-us/research/publication/collecting-telemetry-data-privately/ Collecting telemetry data privately] by Bolin Ding, Jana Kulkarni, Sergey Yekhanin. NIPS 2017.</ref>
 <br>