Privately vertically mining of sequential patterns based on differential privacy with high efficiency and utility.
Privately vertically mining of sequential patterns based on differential privacy with high efficiency and utility.
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DOI：
10.1038/s41598-023-43030-z
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发表时间：
2023-10-19
影响因子：
4.6
通讯作者：
Yuan, Caihong
中科院分区：
文献类型：
--
作者：
Liang, Wenjuan;Zhang, Wenke;Liang, Songtao;Yuan, Caihong
序列模式挖掘是许多重要数据分析任务的基本工具之一，如Web浏览行为分析。基于频繁模式，决策者可以获得经济收益和社会价值。另一方面，序列数据经常包含敏感信息，直接分析这些数据会从隐私角度引起用户的担忧。差分隐私（DP），作为最流行的隐私模型，已被用来解决这个隐私问题。大多数现有的DP解决方案被设计为将联合收割机水平序列模式挖掘算法与差分隐私相结合。由于水平算法的效率低下，他们的DP解决方案无法在提供高隐私保证的同时确保高效率和准确性。为此，我们提出了一种新的私有序列模式挖掘方案privVertical，该方案将垂直挖掘算法与差分隐私算法相结合，以实现上述目标。与基于水平算法的DP解决方案不同，privVertical可以通过避免执行昂贵的数据库扫描或昂贵的投影数据库构建来提高效率。此外，为了提高准确性，一个差分私有哈希映射表（称为privHashMap）的设计，以记录频繁的并发项目和他们的噪音支持的基础上稀疏向量技术。PrivHashMap用于预剪枝私有挖掘中多余的不频繁候选序列，稀疏向量技术用于提高PrivHashMap的准确性。在对这些无效的候选序列进行剪枝之后，需要较少的噪声来实现相同的隐私级别，从而提高了隐私挖掘的准确性。理论隐私分析证明了privVertical满足-差分隐私。实验表明，在达到相同隐私级别的情况下，privVertical具有更高的准确率和效率。
Sequential pattern mining is one of the fundamental tools for many important data analysis tasks, such as web browsing behavior analysis. Based on frequent patterns, decision-makers can obtain both economic gains and social values. Sequential data, on the other hand, frequently contain sensitive information, and directly analyzing these data will raise user concerns from a privacy perspective. Differential privacy (DP), as the most popular privacy model, has been employed to address this privacy concern. Most existing DP-Solutions are designed to combine horizontal sequence pattern mining algorithms with differential privacy. Due to the inefficiency of horizontal algorithms, their DP-Solutions cannot ensure high efficiency and accuracy while offering a high privacy guarantee. Therefore, we proposed privVertical, a new private sequence pattern mining scheme combining the vertical mining algorithm with differential privacy to achieve the above objective. Unlike DP-solutions based on horizontal algorithms, privVertical can promote efficiency by avoiding performing costly database scans or costly projection database constructions. Moreover, to promote accuracy, a differentially private hash MapList (called privHashMap) is designed to record frequent concurrency items and their noisy support based on the Sparse Vector Technique. PrivHashMap is used to pre-pruning excessive infrequent candidate sequences in private mining, and Sparse Vector Technique is used to promote the accuracy of PrivHashMap. After pruning these invalid candidate sequences, less noise is required to achieve the same level of privacy, increasing the accuracy of private mining. Theoretical privacy analysis proves privVertical satisfies -differential privacy. Experiments show that privVertical achieves higher accuracy and efficiency while achieving the same privacy level.
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影响因子：
7.5
作者：
Zaki, MJ
通讯作者：
Zaki, MJ
影响因子：
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作者：
Nanavati, Nirali R.;Jinwala, Devesh C.
通讯作者：
Jinwala, Devesh C.
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DOI：
10.1016/j.future.2020.04.049
发表时间：
2020-10-01
影响因子：
7.5
作者：
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通讯作者：
Shi, Zhenkui
影响因子：
8.5
作者：
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8.9
作者：
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通讯作者：
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