Model for Preserving Privacy Data in URL Query Strings
Sounith Orhpomma, Vinath Mekthanavanh, Sengthong Soukhavong, Nigran Homdoung, Kittikorn Sasujit, Surapon Riyana · 2024
A challenge of data utilization is how to balance data utility and data privacy. For this reason, several privacy preservation models have been proposed. However, they can be effective and efficient in released datasets. They cannot be used to address privacy violation issues that occur in URL query strings. Therefore, a new privacy preservation model for URL query strings is proposed in this work. Furthermore, we evaluate the proposed model by using extensive experiments. The experimental results show that the proposed model is an effective and efficient URL query string privacy preservation.