NDLI: Utility-based anonymization using local recoding

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Utility-based anonymization using local recoding

Content Provider	ACM Digital Library
Author	Wang, Wei Fu, Ada Wai-Chee Pei, Jian Wang, Xiaoyuan Xu, Jian Shi, Baile
Abstract	Privacy becomes a more and more serious concern in applications involving microdata. Recently, efficient anonymization has attracted much research work. Most of the previous methods use global recoding, which maps the domains of the quasi-identifier attributes to generalized or changed values. However, global recoding may not always achieve effective anonymization in terms of discernability and query answering accuracy using the anonymized data. Moreover, anonymized data is often for analysis. As well accepted in many analytical applications, different attributes in a data set may have different utility in the analysis. The utility of attributes has not been considered in the previous methods.In this paper, we study the problem of utility-based anonymization. First, we propose a simple framework to specify utility of attributes. The framework covers both numeric and categorical data. Second, we develop two simple yet efficient heuristic local recoding methods for utility-based anonymization. Our extensive performance study using both real data sets and synthetic data sets shows that our methods outperform the state-of-the-art multidimensional global recoding methods in both discernability and query answering accuracy. Furthermore, our utility-based method can boost the quality of analysis using the anonymized data.
Starting Page	785
Ending Page	790
Page Count	6
File Format	PDF
ISBN	1595933395
DOI	10.1145/1150402.1150504
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Local recoding K-anonymity Privacy preservation Utility Data mining
Content Type	Text
Resource Type	Article

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