Principal Component Analysis Optimization Research Articles

PPDP-PCAO: An Efficient High-Dimensional Data Releasing Method With Differential Privacy Protection

Privacy protection in data publishing is an extremely important issue that has been the focus of extensive research in recent years. However, the existing methods have a host of limitations, especially for high-dimensional data publishing. Aiming at the problem of poor availability of publishing results caused by “the curse of dimensionality” in high-dimensional data publishing, we present PPDP-PCAO (Privacy Preserving Data Publishing with Principal Component Analysis Optimization) method, which can better address the problem of the lower utility of release results because of the high noise introduced by the curse of dimensionality. PPDP-PCAO improves the Principal Component Analysis (PCA) algorithm by employing the attribute importance, and reduces the dimension of the data with the improved PCA, which reduces the time and space cost. PPDP-PCAO introduces the evaluation mechanism based on mutual-information into data release, which evaluates the data generated by setting the different quantities of principal components to determine the optimal quantities. PPDP-PCAO considers the existence of multi-sensitive attributes in high-dimensional data, while the traditional methods of allocating privacy budgets cannot satisfy the personalized privacy protection. PPDP-PCAO introduces the sensitivity preference, combines the optimal matching theory, and designs the sensitive attribute hierarchical protection strategy. Extensive experimental results on different real datasets demonstrate that PPDP-PCAO not only guarantees the privacy of published dataset, but also significantly improves the accuracy and data utility than other high-dimensional data publishing methods.