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Open Access

IDEA: A Utility-Enhanced Approach to Incomplete Data Stream Anonymization

College of Computer Science, Sichuan University, Chengdu 610065, China
School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
Cyber Science Research Institute, Sichuan University, Chengdu 610065, China
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Abstract

The prevalence of missing values in the data streams collected in real environments makes them impossible to ignore in the privacy preservation of data streams. However, the development of most privacy preservation methods does not consider missing values. A few researches allow them to participate in data anonymization but introduce extra considerable information loss. To balance the utility and privacy preservation of incomplete data streams, we present a utility-enhanced approach for Incomplete Data strEam Anonymization (IDEA). In this approach, a slide-window-based processing framework is introduced to anonymize data streams continuously, in which each tuple can be output with clustering or anonymized clusters. We consider the dimensions of attribute and tuple as the similarity measurement, which enables the clustering between incomplete records and complete records and generates the cluster with minimal information loss. To avoid the missing value pollution, we propose a generalization method that is based on maybe match for generalizing incomplete data. The experiments conducted on real datasets show that the proposed approach can efficiently anonymize incomplete data streams while effectively preserving utility.

Keywords

utilityanonymizationgeneralizationincomplete data streamsprivacy preservation

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Tsinghua Science and Technology
Volume 27 Issue 1,
February 2022
Pages 127-140
DOI: 10.26599/TST.2020.9010031
Cite this article:
Yang L, Chen X, Luo Y, et al. IDEA: A Utility-Enhanced Approach to Incomplete Data Stream Anonymization. Tsinghua Science and Technology, 2022, 27(1): 127-140. https://doi.org/10.26599/TST.2020.9010031

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Received: 14 July 2020
Revised: 26 August 2020
Accepted: 01 September 2020
Published: 17 August 2021
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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