Anonymity for continuous data publishing
k-anonymization is an important privacy protection mechanism in data publishing. While there has been a great deal of work in recent years, almost all considered a single static release. Such mechanisms only protect the data up to the first release or first recipient. In practical applications, data is published continuously as new data arrive; the same data may be anonymized differently for a different purpose or a different recipient. In such scenarios, even when all releases are properly k-anonymized, the anonymity of an individual may be unintentionally compromised if recipient cross-examines all the releases received or colludes with other recipients. Preventing such attacks, called correspondence attacks, faces major challenges. In this paper, we systematically characterize the correspondence attacks and propose an efficient anonymization algorithm to thwart the attacks in the model of continuous data publishing. Copyright 2008 ACM.
