dRiskRMD(sdcMicro)
dRiskRMD()所属R语言包：sdcMicro
RMD based disclosure risk
RMD的披露风险
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Distance-based disclosure risk estimation via robust Mahalanobis Distances.
通过强大的马氏距离为基础的信息披露风险估计。
用法----------Usage----------
dRiskRMD(x, xm, k = 0.01, k2=0.05)
参数----------Arguments----------
参数：x
original data
原始数据
参数：xm
masked data
屏蔽数据
参数：k
weight for adjusting the influence of the robust Mahalanobis distances, i.e. to increase or decrease each of the disclosure risk intervals.
重量用于调节的健壮的马氏距离的影响，即以增加或减少每个披露风险间隔。
参数：k2
parameter for method RMDID2 to choose a small interval around each masked observation.
参数方法RMDID2选择一个小的区间每个蒙面观察周围。
Details
详细信息----------Details----------
This method is an extension of method SDID because it accounts for the “outlyingness” of each observations. This is a quite natural approach since outliers do have a higher risk of re-identification and therefore these outliers should have larger disclosure risk intervals as observations in the center of the data cloud.
这个方法是一个扩展方法SDID，因为它占的的“outlyingness”每个观察。这是一个很自然的方法，因为异常值也有较高的风险识别，因此这些异常应该有较大的泄露风险间隔观测的数据中心云。
The algorithm works as follows:
该算法的工作原理如下：
1. Robust Mahalanobis distances are estimated in order to get a robust multivariate distance for each observation.
1。强大的马氏距离估计，以获得一个强大的多元距离为每个观测。
2. Intervals are estimated for each observation around every data point of the original data points where the length of the interval is defined/weighted by the squared robust Mahalanobis distance and the parameter $k$. The higher the RMD of an observation the larger the interval.
2。的时间间隔估计对于每个观测周围的每一个数据点的原始数据点的时间间隔的长度被定义/平方鲁棒马氏距离和参数$ k $个加权。更高的观察的RMD的间隔越大。
3. Check if the corresponding masked values fall into the intervals around the original values or not. If the value of the corresponding observation is within such an interval the whole observation is considered unsafe. So, we get a whole vector indicating which observation is save or not, and we are finished already when using method RMDID1).
3。检查，如果相应的屏蔽值落入原始值或周围的间隔。如果相应的观测值是在这样一个时间间隔在整个观察被认为是不安全的。因此，我们得到了全矢量观察是保存或不，我们已经完成使用方法RMDID1）时。
4. For method RMDID1w: we return the weighted (via RMD) vector of disclosure risk.
4。对于方法RMDID1w，我们返回披露的风险（通过RMD）的加权矢量。
5. For method RMDID2: whenever an observation is considered unsafe it is checked if $m$ other observations from the masked data are very close (defined by a parameter $k2$ for the length of the intervals as for SDID or RSDID) to such an unsafe observation from the masked data, using Euclidean distances. If more than $m$ points are in such a small interval, we conclude that this observation is “save”.
5。方法RMDID2：观察时，被认为是不安全的，检查是否百万元百万元屏蔽数据的观测非常接近（$ K2作为SDID或RSDID的时间间隔的长度由参数定义的）这样一个不安全的观测的的蒙面数据，使用欧氏距离。如果超过$ M $的点都在一个小的区间，我们认为这个观察“保存”。
值----------Value----------
The disclosure risk.
披露的风险。
参数：risk1
percentage of sensitive observations according to method RMDID1.
百分比敏感观测根据方法RMDID1的。
参数：risk2
standardized version of risk1
标准化的风险1版
参数：wrisk1
amount of sensitive observations according to RMDID1 weighted by their corresponding robust Mahalanobis distances.
量敏感的观测根据RMDID1由它们相应的鲁棒马氏距离加权。
参数：wrisk2
RMDID2 measure
RMDID2措施
参数：indexRisk1
index of observations with high risk according to risk1 measure
高风险根据风险1测量指标的观察
参数：indexRisk2
index of observations with high risk according to wrisk2 measure
根据wrisk2措施的高风险指标的观察
（作者）----------Author(s)----------
Matthias Templ
参考文献----------References----------
Robust Statistics Meets SDC: New Disclosure Risk Measures for Continuous Microdata Masking, Lecture Notes in Computer Science, Privacy in Statistical Databases, vol. 5262, pp. 113-126, 2008.
New Developments in Statistical Disclosure Control and Imputation: Robust Statistics Applied to Official Statistics, Suedwestdeutscher Verlag fuer Hochschulschriften, 2009, ISBN: 3838108280, 264 pages.
参见----------See Also----------
dRisk
dRisk
实例----------Examples----------
data(Tarragona)
x <- Tarragona[, 5:7]
y <- addNoise(x)$xm
dRiskRMD(x, xm=y)
dRisk(x, xm=y)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-29 23:17:21
