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→Randomized response 随机应答
===The Laplace mechanism 拉普拉斯机制===
===The Laplace mechanism 拉普拉斯机制===
{{See also|Additive noise mechanisms}}
{{See also|Additive noise mechanisms添加噪声机制}}
The Laplace mechanism adds Laplace noise (i.e. noise from the [[Laplace distribution]], which can be expressed by probability density function <math>\text{noise}(y)\propto \exp(-|y|/\lambda)\,\!</math>, which has mean zero and standard deviation <math>\sqrt{2} \lambda\,\!</math>). Now in our case we define the output function of <math>\mathcal{A}\,\!</math> as a real valued function (called as the transcript output by <math>\mathcal{A}\,\!</math>) as <math>\mathcal{T}_{\mathcal{A}}(x)=f(x)+Y\,\!</math> where <math>Y \sim \text{Lap}(\lambda)\,\!\,\!</math> and <math>f\,\!</math> is the original real valued query/function we planned to execute on the database. Now clearly <math>\mathcal{T}_{\mathcal{A}}(x)\,\!</math> can be considered to be a continuous random variable, where
The Laplace mechanism adds Laplace noise (i.e. noise from the [[Laplace distribution]], which can be expressed by probability density function <math>\text{noise}(y)\propto \exp(-|y|/\lambda)\,\!</math>, which has mean zero and standard deviation <math>\sqrt{2} \lambda\,\!</math>). Now in our case we define the output function of <math>\mathcal{A}\,\!</math> as a real valued function (called as the transcript output by <math>\mathcal{A}\,\!</math>) as <math>\mathcal{T}_{\mathcal{A}}(x)=f(x)+Y\,\!</math> where <math>Y \sim \text{Lap}(\lambda)\,\!\,\!</math> and <math>f\,\!</math> is the original real valued query/function we planned to execute on the database. Now clearly <math>\mathcal{T}_{\mathcal{A}}(x)\,\!</math> can be considered to be a continuous random variable, where
===Randomized response 随机应答===
===Randomized response 随机应答===
{{See also|Local differential privacy}}
{{See also|Local differential privacy局域差分隐私}}
A simple example, especially developed in the [[social sciences]],<ref name=":7">{{cite journal |last=Warner |first=S. L. |date=March 1965 |title=Randomised response: a survey technique for eliminating evasive answer bias |jstor=2283137 |journal=[[Journal of the American Statistical Association]] |publisher=[[Taylor & Francis]] |volume=60 |issue=309 |pages=63–69 |doi= 10.1080/01621459.1965.10480775|pmid=12261830 }}</ref> is to ask a person to answer the question "Do you own the ''attribute A''?", according to the following procedure:
A simple example, especially developed in the [[social sciences]],<ref name=":7">{{cite journal |last=Warner |first=S. L. |date=March 1965 |title=Randomised response: a survey technique for eliminating evasive answer bias |jstor=2283137 |journal=[[Journal of the American Statistical Association]] |publisher=[[Taylor & Francis]] |volume=60 |issue=309 |pages=63–69 |doi= 10.1080/01621459.1965.10480775|pmid=12261830 }}</ref> is to ask a person to answer the question "Do you own the ''attribute A''?", according to the following procedure:
==现实世界中差分隐私算法的应用==
==现实世界中差分隐私算法的应用==
{{see also|Implementations of differentially private analyses 参见：差分隐私分析的实践}}
{{see also|Implementations of differentially private analyses差分隐私分析的实践}}
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" />