PRESERVING DATABASE SCHEMA PRIVACY WHILE GENERATING SQL QUERIES USING GENERATIVE AI

Abstract

In Generative AI (GenAI) models have proven effective in translating natural language queries into SQL. However, directly exposing database schema details, including table and column names, to GenAI poses significant security and privacy concerns. This paper presents an approach to preserve database schema integrity by employing aliasing techniques. Aliased representations of schema elements are enriched with semantic metadata, ensuring GenAI can generate accurate SQL queries without direct access to the original schema. The proposed method bridges the gap between privacy and functionality, providing a robust framework for secure and efficient SQL generation.