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.