A K-anonymity model with strongly identifiable attributes

In empirical studies of protecting privacy via anonymity, sensitive attributes are typically studied. Through models or algorithms, researchers guarantee some or all of their private information, resulting in a directed method. Sensitive attributes often are deleted until few. This paper analyzes a unique view of quasi-identifiers and shows that the distribution of quasi-identifiers is far from insignificant. In every information release, without exception, we find that there exists a ranking for quasi-identifiers, from low to high, such that almost all published information consist of lower-ranked quasi-identifiers with higher-ranked ones. We present a k-anonymity model with strongly identifiable attributes for deducing such rankings from observed published data. We hold the view that the rankings produced reflect a method of privacy protection.