Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy

IF 1.2 3区计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Journal of Computer Science and Technology Pub Date : 2023-12-01 DOI:10.1007/s11390-023-1346-7

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Abstract

Local differential privacy (LDP) approaches to collecting sensitive information for frequent itemset mining (FIM) can reliably guarantee privacy. Most current approaches to FIM under LDP add “padding and sampling” steps to obtain frequent itemsets and their frequencies because each user transaction represents a set of items. The current state-of-the-art approach, namely set-value itemset mining (SVSM), must balance variance and bias to achieve accurate results. Thus, an unbiased FIM approach with lower variance is highly promising. To narrow this gap, we propose an Item-Level LDP frequency oracle approach, named the Integrated-with-Hadamard-Transform-Based Frequency Oracle (IHFO). For the first time, Hadamard encoding is introduced to a set of values to encode all items into a fixed vector, and perturbation can be subsequently applied to the vector. An FIM approach, called optimized united itemset mining (O-UISM), is proposed to combine the padding-and-sampling-based frequency oracle (PSFO) and the IHFO into a framework for acquiring accurate frequent itemsets with their frequencies. Finally, we theoretically and experimentally demonstrate that O-UISM significantly outperforms the extant approaches in finding frequent itemsets and estimating their frequencies under the same privacy guarantee.

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局部差异隐私下基于哈达玛编码的常项集挖掘

摘要为频繁项集挖掘（FIM）收集敏感信息的局部差分隐私（LDP）方法可以可靠地保证隐私。由于每笔用户交易都代表一组项目，因此目前大多数 LDP 下的频繁项集挖掘方法都增加了 "填充和采样 "步骤，以获得频繁项集及其频率。目前最先进的方法，即集值项集挖掘（SVSM），必须在方差和偏差之间取得平衡，才能获得准确的结果。因此，一种无偏见、方差较小的 FIM 方法大有可为。为了缩小这一差距，我们提出了一种项级 LDP 频率甲骨文方法，名为基于哈达玛德变换的集成频率甲骨文（IHFO）。我们首次在一组值中引入哈达玛编码，将所有项目编码为一个固定的向量，随后可对该向量进行扰动。我们提出了一种称为优化联合项集挖掘（O-UISM）的 FIM 方法，将基于填充和采样的频率神谕（PSFO）和 IHFO 结合到一个框架中，以获取精确的频繁项集及其频率。最后，我们通过理论和实验证明，O-UISM 在寻找频繁项集和估算其频率方面明显优于现有方法。

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来源期刊

Journal of Computer Science and Technology 工程技术-计算机：软件工程

CiteScore

4.00

自引率

0.00%

发文量

2255

审稿时长

9.8 months

期刊介绍： Journal of Computer Science and Technology (JCST), the first English language journal in the computer field published in China, is an international forum for scientists and engineers involved in all aspects of computer science and technology to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. While the journal emphasizes the publication of previously unpublished materials, selected conference papers with exceptional merit that require wider exposure are, at the discretion of the editors, also published, provided they meet the journal''s peer review standards. The journal also seeks clearly written survey and review articles from experts in the field, to promote insightful understanding of the state-of-the-art and technology trends. Topics covered by Journal of Computer Science and Technology include but are not limited to: -Computer Architecture and Systems -Artificial Intelligence and Pattern Recognition -Computer Networks and Distributed Computing -Computer Graphics and Multimedia -Software Systems -Data Management and Data Mining -Theory and Algorithms -Emerging Areas