SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network

Data publishing techniques have led to breakthroughs in several areas. These tools provide a promising direction. However, when they are applied private or sensitive data such as patient medical records, the published may divulge critical information. In order address this issue, we propose differential method (SSKM_DP) based on SFLA-Kohonen network, which perturbs attributes maximum information coefficient achieve trade-off between security and usability. Additionally, introduced single-population frog jump algorithm (SFLA) optimize network. Extensive experiments benchmark datasets demonstrated that SSKM_DP outperforms state-of-the-art methods for differentially significantly.