Data Anonymization that Leads to the Most Accurate Estimates of Statistical Characteristics: Fuzzy-Motivated Approach.
Data Anonymization that Leads to the Most Accurate Estimates of Statistical Characteristics: Fuzzy-Motivated Approach.
复制标题
数据匿名化可实现最准确的统计特征估计：模糊驱动方法。
DOI：
10.1109/ifsa-nafips.2013.6608471
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Kosheleva,O
中科院分区：
文献类型：
--
作者：
Xiang,G;Ferson,S;Ginzburg,L;Longpré,L;Mayorga,E;Kosheleva,O
为了保护隐私，原始数据点（具有精确值）被包含每个（不可访问的）数据点的框所取代。这种隐私动机的不确定性导致了基于这些数据计算的统计特征的不确定性。在以前的论文中，我们描述了如何在假设我们对所需特征使用相同的标准统计估计的情况下最小化这种不确定性。在本文中，我们表明，我们可以进一步减少由此产生的不确定性，如果我们允许模糊动机的加权估计，我们解释了如何最佳地选择相应的权重。
To preserve privacy, the original data points (with exact values) are replaced by boxes containing each (inaccessible) data point. This privacy-motivated uncertainty leads to uncertainty in the statistical characteristics computed based on this data. In a previous paper, we described how to minimize this uncertainty under the assumption that we use the same standard statistical estimates for the desired characteristics. In this paper, we show that we can further decrease the resulting uncertainty if we allow fuzzy-motivated weighted estimates, and we explain how to optimally select the corresponding weights.