$\tau$ -Safe ( $l,k$ )-Diversity Privacy Model for Sequential Publication With High Utility

Preserving privacy while maintaining high utility during sequential publication for data providers and data users in mathematical statistics, scientific researching, and organizations making decisions play an important role recently. The
$\tau $
-safety model is the state-of-the-art model in sequential publication. However, it is based on the generalization technique, which has some drawbacks such as heavy information loss and difficulty of supporting marginal publication. Besides, the privacy of individuals is the major aspect that needs to be protected in privacy preserving data publishing. In this paper, to protect the privacy of individuals in sequential publication, we develop a new
$\tau $
-safe (
$l,k$
)-diversity privacy model based on generalization and segmentation by record anonymity satisfying
$l$
-diversity and individual anonymity satisfying
$k$
-anonymity. This privacy model ensures that each record’s signatures keep consistency or have no intersection in all releases. It can get high data utility while resisting the linking attacks due to arbitrary updates. In addition, it can also be applied to a dataset where individual has multiple records and arbitrary marginal publication. The results of our experiments show that the proposed privacy model achieves better anonymization quality and query accuracy in comparison with the
$m$
-invariance and
$\tau $
-safety model in the sequential publication with arbitrary updates.