测量预发布数据集隐私级别的方法

Dan Wang, Bing Guo, Yan Shen
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引用次数: 2

摘要

为了在数据发布中保护个人隐私信息,设计了几种隐私保护技术。在不测量数据集所需的隐私保护强度的情况下,通常很容易造成数据集的额外信息丢失。为了应用适当的隐私保护强度,作者提出了隐私评分,这是一种对预发布数据集中包含的隐私信息进行综合评价的新指标。使用此度量,发布者可以根据其所属的隐私级别对预发布数据集应用隐私技术。隐私得分由预先发布的数据集所包含的隐私信息的数量和质量决定。此外,作者提出了一个基于层次分析法的数据敏感性模型,为敏感属性的每个可能值分配敏感性分数。利用成人数据集验证了该方法的合理性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method for measuring the privacy level of pre-published dataset
Several privacy protection technologies have been designed for protecting individuals' privacy information in data publishing. It is often easy to make additional information loss of a dataset without measuring the strength of privacy protection it required. To apply appropriate strength of privacy preservation, the authors put forward privacy score, a new metric for making a comprehensive evaluation of the privacy information contained in the pre-published dataset. Using this measure, publishers can apply the privacy techniques to the pre-published dataset in accordance with the privacy level it belongs to. The privacy score is determined by the amount as well as the quality of privacy information in which the pre-published dataset is contained. Furthermore, the authors present a data sensitivity model based on analytic hierarchy process for assigning a sensitivity score to each possible value of a sensitive attribute. The reasonability and effectiveness of the proposed approach are verified by using the Adult dataset.
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