IDEA: A utility-enhanced approach to incomplete data stream anonymization

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