PRESERVING DATABASE SCHEMA PRIVACY WHILE GENERATING SQL QUERIES USING GENERATIVE AI

ICTACT Journal on Soft Computing ( Volume: 15 , Issue: 3 )

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.

Authors

Jitesh Prasad Khatick1, Soumitra Kumar Mandal2
Maulana Abul Kalam Azad University of Technology, India1, National Institute of Technical Teachers’ Training and Research, India2

Keywords

NLIDB (Natural Language Interface to Database), SQL (Structured Query Language), StanfordCoreNLP, Large Language Model (LLM), Generative AI (GenAI)

Published By
ICTACT
Published In
ICTACT Journal on Soft Computing
( Volume: 15 , Issue: 3 )
Date of Publication
January 2025
Pages
3662 - 3668
Page Views
475
Full Text Views
64

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