通信学报
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丁丽萍,卢国庆
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摘要: 频繁模式挖掘是数据挖掘的一个基本问题,其模式本身和相应计数都有可能泄露隐私信息。当前,差分隐私通过添加噪音使数据失真,有效实现了隐私保护的目的。首先介绍了差分隐私保护模型的理论基础;其次,详细综述了差分隐私下3种典型的频繁模式挖掘方法的最新研究进展,并进行对比性分析;最后对未来的研究方向进行了展望。
Abstract: Frequent pattern mining is an exploratory problem in the field of data mining. However, directly releasing the discovered frequent patterns and the corresponding true supports may reveal the individuals’ privacy. The state-of-the-art solution for this problem is differential privacy, which offers a strong degree of privacy protection by adding noise. Firstly, the theoretical basis of differential privacy was introduced. Then, three representative frequent pattern mining methods under differential privacy were summarized and compared in detail. Finally, some future research directions were discussed.
丁丽萍,卢国庆. 面向频繁模式挖掘的差分隐私保护研究综述[J]. 通信学报.
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https://www.infocomm-journal.com/txxb/CN/Y2014/V35/I10/23