  Most existing anonymization work has been done on static datasets, which have
no update and need only one-time publication. Recent studies consider
anonymizing dynamic datasets with external updates: the datasets are updated
with record insertions and/or deletions. This paper addresses a new problem:
anonymous re-publication of datasets with internal updates, where the attribute
values of each record are dynamically updated. This is an important and
challenging problem for attribute values of records are updating frequently in
practice and existing methods are unable to deal with such a situation.
  We initiate a formal study of anonymous re-publication of dynamic datasets
with internal updates, and show the invalidation of existing methods. We
introduce theoretical definition and analysis of dynamic datasets, and present
a general privacy disclosure framework that is applicable to all anonymous
re-publication problems. We propose a new counterfeited generalization
principle alled m-Distinct to effectively anonymize datasets with both external
updates and internal updates. We also develop an algorithm to generalize
datasets to meet m-Distinct. The experiments conducted on real-world data
demonstrate the effectiveness of the proposed solution.
