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Several uses of differential privacy in practice are known to date:
Several uses of differential privacy in practice are known to date:
*2008: [[United States Census Bureau|U.S. Census Bureau]], for showing commuting patterns.<ref name="MachanavajjhalaKAGV08" />
*2008: [[United States Census Bureau|U.S. Census Bureau]], for showing commuting patterns.<ref name="MachanavajjhalaKAGV08" />
* 2014: [[Google]]'s RAPPOR, for telemetry such as learning statistics about unwanted software hijacking users' settings. <ref name="RAPPOR" /><ref>{{Citation|title=google/rappor|date=2021-07-15|url=https://github.com/google/rappor|publisher=GitHub}}</ref>
* 2014: [[Google]]'s RAPPOR, for telemetry such as learning statistics about unwanted software hijacking users' settings. <ref name="RAPPOR" /><ref name=":12">{{Citation|title=google/rappor|date=2021-07-15|url=https://github.com/google/rappor|publisher=GitHub}}</ref>
*2015: Google, for sharing historical traffic statistics.<ref name="Eland" />
*2015: Google, for sharing historical traffic statistics.<ref name="Eland" />
*2016: [[Apple Inc.|Apple]] announced its intention to use differential privacy in [[iOS 10]] to improve its [[Intelligent personal assistant]] technology.<ref>{{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>
*2016: [[Apple Inc.|Apple]] announced its intention to use differential privacy in [[iOS 10]] to improve its [[Intelligent personal assistant]] technology.<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>
*2017: Microsoft, for telemetry in Windows.<ref name="DpWinTelemetry" />
*2017: Microsoft, for telemetry in Windows.<ref name="DpWinTelemetry" />
*2019: Privitar Lens is an API using differential privacy.<ref>{{cite web|title=Privitar Lens|url=https://www.privitar.com/privitar-lens|access-date=20 February 2018}}</ref>
*2019: Privitar Lens is an API using differential privacy.<ref name=":14">{{cite web|title=Privitar Lens|url=https://www.privitar.com/privitar-lens|access-date=20 February 2018}}</ref>
* 2020: LinkedIn, for advertiser queries.<ref name="DpLinkedIn" />
* 2020: LinkedIn, for advertiser queries.<ref name="DpLinkedIn" />
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，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" />
*2008: U.S. Census Bureau, for showing commuting patterns.
*2015：Google，分享历史流量统计数据。<ref name="Eland" />
*2014: Google's RAPPOR, for telemetry such as learning statistics about unwanted software hijacking users' settings.
*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，Windows遥测系统。<ref name="DpWinTelemetry" />
*2019：priveritar Lens是一个使用差分隐私技术的 API。<ref name=":14" />
*2020：LinkedIn，供广告商查询。<ref name="DpLinkedIn" />
*2019: Privitar Lens is an API using differential privacy.
*2020: LinkedIn, for advertiser queries.
*2008: 美国人口普查局，显示通勤模式。
*2014年: 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。2015: Google，分享历史流量统计数据。
*2016年: 苹果公司宣布打算在 iOS 10中使用差分隐私智能个人助理来改进其智能个人助理技术。
==Public purpose considerations ==
==Public purpose considerations ==
There are several public purpose considerations regarding differential privacy that are important to consider, especially for policymakers and policy-focused audiences interested in the social opportunities and risks of the technology:<ref>{{Cite web|title=Technology Factsheet: Differential Privacy|url=https://www.belfercenter.org/publication/technology-factsheet-differential-privacy|access-date=2021-04-12|website=Belfer Center for Science and International Affairs|language=en}}</ref>
There are several public purpose considerations regarding differential privacy that are important to consider, especially for policymakers and policy-focused audiences interested in the social opportunities and risks of the technology:<ref name=":15">{{Cite web|title=Technology Factsheet: Differential Privacy|url=https://www.belfercenter.org/publication/technology-factsheet-differential-privacy|access-date=2021-04-12|website=Belfer Center for Science and International Affairs|language=en}}</ref>
关于差分隐私技术，有几个重要的公共目标的问题需要考虑，特别是对于那些对此技术的社会机遇和风险感兴趣的决策者和政策聚焦的受众:<ref name=":15" />
*'''Data Utility & Accuracy.''' The main concern with differential privacy is the tradeoff between data utility and individual privacy. If the privacy loss parameter is set to favor utility, the privacy benefits are lowered (less “noise” is injected into the system); if the privacy loss parameter is set to favor heavy privacy, the accuracy and utility of the dataset are lowered (more “noise” is injected into the system). It is important for policymakers to consider the tradeoffs posed by differential privacy in order to help set appropriate best practices and standards around the use of this privacy preserving practice, especially considering the diversity in organizational use cases. It is worth noting, though, that decreased accuracy and utility is a common issue among all statistical disclosure limitation methods and is not unique to differential privacy. What is unique, however, is how policymakers, researchers, and implementers can consider mitigating against the risks presented through this tradeoff.
*'''Data Utility & Accuracy.''' The main concern with differential privacy is the tradeoff between data utility and individual privacy. If the privacy loss parameter is set to favor utility, the privacy benefits are lowered (less “noise” is injected into the system); if the privacy loss parameter is set to favor heavy privacy, the accuracy and utility of the dataset are lowered (more “noise” is injected into the system). It is important for policymakers to consider the tradeoffs posed by differential privacy in order to help set appropriate best practices and standards around the use of this privacy preserving practice, especially considering the diversity in organizational use cases. It is worth noting, though, that decreased accuracy and utility is a common issue among all statistical disclosure limitation methods and is not unique to differential privacy. What is unique, however, is how policymakers, researchers, and implementers can consider mitigating against the risks presented through this tradeoff.
* Data Utility & Accuracy. The main concern with differential privacy is the tradeoff between data utility and individual privacy. If the privacy loss parameter is set to favor utility, the privacy benefits are lowered (less “noise” is injected into the system); if the privacy loss parameter is set to favor heavy privacy, the accuracy and utility of the dataset are lowered (more “noise” is injected into the system). It is important for policymakers to consider the tradeoffs posed by differential privacy in order to help set appropriate best practices and standards around the use of this privacy preserving practice, especially considering the diversity in organizational use cases. It is worth noting, though, that decreased accuracy and utility is a common issue among all statistical disclosure limitation methods and is not unique to differential privacy. What is unique, however, is how policymakers, researchers, and implementers can consider mitigating against the risks presented through this tradeoff.
*'''数据的效用及准确性'''。差分隐私的主要关注点在于数据效用和个人隐私之间的权衡。如果将隐私损失参数设置为有利于实用性，则隐私性降低（向系统中加入的“噪音”较少）; 如果将隐私损失参数设置为有利于隐私性，则数据集的准确性和效用降低（向系统中注入更多的“噪音”）。对于决策者来说，重要的是要考虑差分隐私的权衡，以帮助建立适当的最佳实践标准来使用这种隐私保护机制，特别是考虑到组织用例的多样性。但是值得注意的是，准确性和效用的降低是所有的限制统计数据泄露的方法的共同问题，而并非只属于差分隐私。然而，独特之处在于，决策者、研究人员和实施者可以考虑如何减轻这种权衡带来的风险。
*'''Data Privacy & Security.''' Differential privacy provides a quantified measure of privacy loss and an upper bound and allows curators to choose the explicit tradeoff between privacy and accuracy. It is robust to still unknown privacy attacks. However, it encourages greater data sharing, which if done poorly, increases privacy risk. Differential privacy implies that privacy is protected, but this depends very much on the privacy loss parameter chosen and may instead lead to a false sense of security. Finally, though it is robust against unforeseen future privacy attacks, a countermeasure may be devised that we cannot predict.
*'''Data Privacy & Security.''' Differential privacy provides a quantified measure of privacy loss and an upper bound and allows curators to choose the explicit tradeoff between privacy and accuracy. It is robust to still unknown privacy attacks. However, it encourages greater data sharing, which if done poorly, increases privacy risk. Differential privacy implies that privacy is protected, but this depends very much on the privacy loss parameter chosen and may instead lead to a false sense of security. Finally, though it is robust against unforeseen future privacy attacks, a countermeasure may be devised that we cannot predict.
*Data Privacy & Security. Differential privacy provides a quantified measure of privacy loss and an upper bound and allows curators to choose the explicit tradeoff between privacy and accuracy. It is robust to still unknown privacy attacks. However, it encourages greater data sharing, which if done poorly, increases privacy risk. Differential privacy implies that privacy is protected, but this depends very much on the privacy loss parameter chosen and may instead lead to a false sense of security. Finally, though it is robust against unforeseen future privacy attacks, a countermeasure may be devised that we cannot predict.
*'''数据隐私及安全'''。差分隐私提供了一个量化的隐私损失度量和上限，并允许管理者在隐私和准确性之间做出明确的权衡。它对仍然未知的隐私攻击具有鲁棒性。然而，它鼓励更大的数据共享——如果做得不好，会增加隐私风险。差分隐私意味着隐私是受到保护的，但这在很大程度上取决于选择的隐私损失参数，并可能会导致对安全性的错误认识。最后，尽管它对未来不可预见的隐私攻击具有鲁棒性，但可能会有我们无法预测的对策被设计出来。
==See also==
==See also==
*[[Differentially private analysis of graphs]]
*[[Differentially private analysis of graphs]]
*[[Protected health information]]
*[[Protected health information]]
*准标识符
*准标识符
*指数机制(差分隐私)-一种设计不同私有算法的技术
*指数机制(差分隐私)——一种设计差分隐私算法的技术
* k-匿名
* k-匿名
*图的不同私有分析
*图的差分隐私分析
*受保护的健康信息
*受保护的健康信息
*[https://research.googleblog.com/2014/10/learning-statistics-with-privacy-aided.html Learning Statistics with Privacy, aided by the Flip of a Coin] by Úlfar Erlingsson, Google Research Blog, October 2014
*[https://research.googleblog.com/2014/10/learning-statistics-with-privacy-aided.html Learning Statistics with Privacy, aided by the Flip of a Coin] by Úlfar Erlingsson, Google Research Blog, October 2014
*[https://www.belfercenter.org/publication/technology-factsheet-differential-privacy Technology Factsheet: Differential Privacy] by Raina Gandhi and Amritha Jayanti, Belfer Center for Science and International Affairs, Fall 2020
*[https://www.belfercenter.org/publication/technology-factsheet-differential-privacy Technology Factsheet: Differential Privacy] by Raina Gandhi and Amritha Jayanti, Belfer Center for Science and International Affairs, Fall 2020
差分隐私: Cynthia Dwork，ICALP July 2006。差分隐私的算法基础》 ，Cynthia Dwork 和 Aaron Roth，2014年。2013年12月，加州理工学院卡特里娜 · 利格特教授，差分隐私，差分隐私，差分隐私实用指南，克里斯汀 · 特拉克，普渡大学,2012年4月
差分隐私: Cynthia Dwork，ICALP July 2006。差分隐私的算法基础》 ，Cynthia Dwork 和 Aaron Roth，2014年。2013年12月，加州理工学院卡特里娜 · 利格特教授，差分隐私，差分隐私，差分隐私实用指南，克里斯汀 · 特拉克，普渡大学,2012年4月