  As people's daily life becomes increasingly inseparable from various mobile
electronic devices, relevant service application platforms and network
operators can collect numerous individual information easily. When releasing
these data for scientific research or commercial purposes, users' privacy will
be in danger, especially in the publication of spatiotemporal trajectory
datasets. Therefore, to avoid the leakage of users' privacy, it is necessary to
anonymize the data before they are released. However, more than simply removing
the unique identifiers of individuals is needed to protect the trajectory
privacy, because some attackers may infer the identity of users by the
connection with other databases. Much work has been devoted to merging multiple
trajectories to avoid re-identification, but these solutions always require
sacrificing data quality to achieve the anonymity requirement. In order to
provide sufficient privacy protection for users' trajectory datasets, this
paper develops a study on trajectory privacy against re-identification attacks,
proposing a trajectory K-anonymity model based on Point Density and Partition
(KPDP). Our approach improves the existing trajectory generalization
anonymization techniques regarding trajectory set partition preprocessing and
trajectory clustering algorithms. It successfully resists re-identification
attacks and reduces the data utility loss of the k-anonymized dataset. A series
of experiments on a real-world dataset show that the proposed model has
significant advantages in terms of higher data utility and shorter algorithm
execution time than other existing techniques.
