Author
Ji-Won Byun, Tiancheng Li, Elisa Bertino, Ninghui Li, and Yonglak Sohn
Abstract
Although the k-anonymity and l-diversity models have led to a
number of valuable privacy-protecting techniques and algorithms, the
existing solutions are currently limited to static data release.
That is, it is assumed that a complete dataset is available at the
time of data release. This assumption implies a significant
shortcoming, as in many applications data collection is rather a
continual process. Moreover, the assumption entails ``one-time''
data dissemination; thus, it does not adequately address today's
strong demand for immediate and up-to-date information. In this
paper, we consider incremental data dissemination, where a dataset
is continuously incremented with new data. The key issue here is
that the same data may be anonymized and published multiple times,
each of the time in a different form. Thus, static anonymization
(i.e., anonymization which does not consider previously released
data) may enable various types of inference. In this paper, we
identify such inference issues and discuss some prevention methods.
