Privacy Threats and Privacy Preservation in Multiple Data Releases of High-Dimensional Datasets

Determining how to balance data utilities and data privacy when datasets are released to be utilized outside the scope of data-collecting organizations constitutes a major challenge. To achieve this aim in data collection (datasets), several privacy preservation models have been proposed, such as k-Anonymity and l-Diversity. Unfortunately, these privacy preservation models may be insufficient to address privacy violation issues in datasets that have high-dimensional attributes. For this reason, the privacy preservation models, km-Anonymity and LKC-Privacy, for addressing privacy violation issues in high-dimensional datasets are proposed. However, these privacy preservation models still exhibit privacy violation issues from using data comparison attacks, and they further have data utility issues that must be addressed. Therefore, a privacy preservation model can address privacy violation issues in high-dimensional datasets to be proposed in this work, such that there are no concerns about privacy violations in released datasets from data comparison attacks, and it is highly efficient and effective in data maintenance. Furthermore, we show that the proposed model is efficient and effective through extensive experiments.