Top-k closed co-occurrence patterns mining with differential privacy over multiple streams
Top-k closed co-occurrence patterns mining with differential privacy over multiple streams
复制标题
多流上具有差分隐私的 Top-k 封闭共现模式挖掘
DOI：
10.1016/j.future.2020.04.049
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发表时间：
2020-10-01
影响因子：
7.5
通讯作者：
Shi, Zhenkui
中科院分区：
文献类型：
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作者：
Wang, Jinyan;Fang, Shijian;Shi, Zhenkui
数据流上的频繁模式挖掘在许多应用中是一个非常重要的问题。然而，许多研究都是针对单个流进行的，其中每个交易都是独立的，没有考虑到某些交易是由同一个人产生的。一些现实世界的应用涉及多个流，不断产生的对象，和有趣的观察是在许多流中出现的对象，如新兴的主题发现，电子商务，Web使用模式挖掘和基于位置的服务。本文分析了在多个流上挖掘top-k闭同现模式时，由于一个窗口的单次释放和连续窗口的连续释放所引起的隐私问题。为了防止隐私泄露，提出了一种基于指数机制和拉普拉斯机制的多数据流差异私有top-k闭共现模式挖掘算法.该算法由相异度计算阶段和差异私有挖掘阶段组成，差异私有挖掘阶段包括：使用分裂事务调整CP-Graph，扰动CP-Graph获得top-k闭共生模式候选集，对模式的支持度添加噪声。最后，我们证明了我们的算法满足差分隐私，实验结果表明我们的算法的实用性和效率。(C)2020爱思唯尔B. V.保留所有权利。
The frequent pattern mining over data streams is a very important problem for many applications. However, many researches investigate a single stream in which every transaction is independent and it is not considered that some transactions are generated by the same individual. Some real-world applications involve multiple streams that continuously generate objects, and interesting observations are the objects appearing in many streams, such as emerging topic discovery, e-commerce, web usage pattern mining and location-based services. In this paper, we analyze the privacy problems in mining top-k closed co-occurrence patterns over multiple streams caused by single release of a window and continuous releases in successive windows. To prevent privacy leakage, we propose a differentially private top-k closed co-occurrence patterns mining algorithm across multiple streams with exponential mechanism and Laplace mechanism. The algorithm consists of dissimilarity calculation phase and differentially private mining phase, where differentially private mining phase includes adjusting CP-Graph with splitting transaction, perturbing CP-Graph to obtain the top-k closed co-occurrence patterns candidate set and adding noise to the supports of patterns. Finally, we prove our algorithm satisfies differential privacy and experiment results show the utility and efficiency of our algorithm. (C) 2020 Elsevier B.V. All rights reserved.