Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy
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...
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| Published in | Journal of computer science and technology Vol. 38; no. 6; pp. 1403 - 1422 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Singapore
Springer Nature Singapore
01.12.2023
Springer Nature B.V |
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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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| AbstractList | 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. 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 pro-posed 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. |
| Author | Liu, Rui-Xuan Zhao, Dan Zhang, Xiao-Ying Li, Cui-Ping Chen, Hong Zhao, Su-Yun |
| Author_xml | – sequence: 1 givenname: Dan surname: Zhao fullname: Zhao, Dan organization: Institute of Scientific and Technical Information of China, Key Laboratory of Data Engineering and Knowledge Engineering (Ministry of Education), School of Information, Renmin University of China – sequence: 2 givenname: Su-Yun surname: Zhao fullname: Zhao, Su-Yun organization: Key Laboratory of Data Engineering and Knowledge Engineering (Ministry of Education), School of Information, Renmin University of China – sequence: 3 givenname: Hong surname: Chen fullname: Chen, Hong email: chong@ruc.edu.cn organization: Key Laboratory of Data Engineering and Knowledge Engineering (Ministry of Education), School of Information, Renmin University of China – sequence: 4 givenname: Rui-Xuan surname: Liu fullname: Liu, Rui-Xuan organization: Key Laboratory of Data Engineering and Knowledge Engineering (Ministry of Education), School of Information, Renmin University of China – sequence: 5 givenname: Cui-Ping surname: Li fullname: Li, Cui-Ping organization: Key Laboratory of Data Engineering and Knowledge Engineering (Ministry of Education), School of Information, Renmin University of China – sequence: 6 givenname: Xiao-Ying surname: Zhang fullname: Zhang, Xiao-Ying organization: Key Laboratory of Data Engineering and Knowledge Engineering (Ministry of Education), School of Information, Renmin University of China |
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| Copyright | Institute of Computing Technology, Chinese Academy of Sciences 2024 Institute of Computing Technology, Chinese Academy of Sciences 2024. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| SubjectTerms | Artificial Intelligence Coding Computer Science Data mining Data Structures and Information Theory Information Systems Applications (incl.Internet) Mining Perturbation Privacy Regular Paper Sampling Software Engineering Theory of Computation |
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| Title | Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy |
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