【摘 要】
：
K-anonymity privacy model is a typical model to protect privacy when disseminating data involving individual subjects.The drawback of k-anonymity is that generalization will result in considerable los
【机 构】
：
Hebei Normal University of Science and Technology,Qinhuangdao 066004,China
【出 处】
：
2014全国理论计算机科学学术年会
论文部分内容阅读
K-anonymity privacy model is a typical model to protect privacy when disseminating data involving individual subjects.The drawback of k-anonymity is that generalization will result in considerable loss of information.Further,under limited information loss an exhaustive analysis is required to determine a k value based on the privacy requirement by data publisher.Studies in this context have so far focused on minimizing the information loss for some given value of k,how to optimize k value that fits the specified data quality and privacy requirement is sparse.In this paper,we formulate a multi-objective optimization problem of k to illustrate that the decision of k can be much more exible.At first,one k bound is analyzed basing on the privacy disclosure,and another k bound is analyzed basing on the data quality metric.Then,the optimal k-value is gained by intersecting the two bounds.At last,an algorithm is employed to provide multi-objective optimization of k that is win-win on privacy requirement and data quality.
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