Research on Preprocessing Methods for Aggregate Queries in Data Sharing

This work presents an in-depth investigation into the preprocessing methods for aggregate queries in data sharing, with a focus on enhancing privacy preservation and efficiency within big data platforms. The rapid growth of big data has necessitated the sharing of detailed data through various applications, which often involves aggregate queries. However, current preprocessing techniques for these queries have limitations, including inadequate protection of sensitive data and inflexibility in handling dynamic combinations of dimension fields. Addressing these challenges, our research introduces a novel approach that employs a Smart University Bigdata Platform to configure and manage aggregate queries, while implementing advanced desensitization rules to safeguard sensitive information. The study aims to provide a robust solution that not only secures data privacy but also maintains the flexibility and efficiency of aggregate queries across different databases.