Enhancing Privacy and Utility in Big Data Publishing through Attribute Selection Optimization
Vaishali Chauhan, Ruchika Gupta · 2024
In the era of big data, it is undoubtedly vital to extract relevant data from databases. The rise of big data and technical breakthroughs has raised enormous privacy and security concerns. Our research addresses the elaborate process of ensuring privacy while propagating information, emphasizing database attribute selection. By preserving quasi-identifier attributes, our method minimizes re-identification risks and improves the models. This approach optimizes privacy, data loss, and data utility by carefully categorizing attributes into sensitive and quasi-identifier categories based on unique ratios. This research makes an important contribution to data privacy field of big data publishing, it introduces an organized framework for selecting attributes and preserving privacy.