Transaction Data Collection for Itemset Mining Under Local Differential Privacy
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Clc Number:

TP309

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National Natural Science Foundation of China (61702119, U1711262, U1501252, U1711261); Science and Technology Program of Guangzhou Municipality (201804010236, 201607010152); Basic and Applied Basic Research Foundation of Guangdong Province (2019A1515012048); Innovation Team Project of Education Department of Guangdong Province (2017KCXTD021)

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    Abstract:

    Transaction data is commonly in various application scenarios, such as shopping records, page browsing history, etc., service providers collect and analyze transaction data for providing better services. However, collecting transaction data will disclose privacy information. To solve the problem, this study proposes a transaction data collection mechanism based on condensed local differential privacy (CLDP). Firstly, a new score function of the candidate set is defined. Secondly, the output domain of the candidate set is separated into several subspaces according to the function. Thirdly, the client selects one subspace randomly, and generates transaction data randomly based on the subspace, then, sends it to the untrusted data collector. Finally, considering the difficulty for setting the privacy parameter, the heuristic privacy parameter setting strategy is designed based on the maximum posterior confidence threat model (MPC). The theoretical analysis shows that this method can protect the length and content of transaction data at the same time and satisfies a-CLDP. The experiments demonstrate that the transaction data collected in this study has higher utility than the state-of-the-art approaches, and the privacy parameter setting is semantic.

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欧阳佳,印鉴,肖政宏,赵慧民,刘少鹏,梁鹏,肖茵茵.面向频繁项集挖掘的本地差分隐私事务数据收集方法.软件学报,2021,32(11):3541-3562

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History
  • Received:November 06,2019
  • Revised:March 09,2020
  • Adopted:
  • Online: May 21,2021
  • Published: November 06,2021
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