document: Utility-based anonymization using local recoding 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 .
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