Abstract—Several approaches have been proposed to anonymize relational databases using the criterion of k-anonymity, to
avoid the disclosure of sensitive information by re-identification attacks. A relational database is said to meet the criterion of
k-anonymity if each record is identical to at least (k-1) other records in terms of quasi-identifier attribute values. To anonymize a
transactional database and satisfy the constraint of k-anonymity, each item must successively be considered as a quasi-identifier
attribute. But this process greatly increases dimensionality, and thus also the computational complexity of anonymization, and
information loss. In this paper, a novel efficient anonymization system called PTA is proposed to not only anonymize transactional
data with a small information loss but also to reduce the computational complexity of the anonymization process. The PTA
system consists of three modules to anonymize transactional data and guarantees that at least k-anonymity is achieved: a preprocessing
module, a Traveling Salesman Problem (TSP) module, and an anonymization module. Extensive experiments have
been carried to compare the efficiency of the designed approach with the state-of-the-art anonymization algorithms in terms of
scalability, runtime, and information loss. Results indicate that the proposed PTA system outperforms the compared algorithms
in all respects.