DP-starJ: A Differential Private Scheme towards Analytical Star-Join Queries

TL;DR

This paper introduces DP-starJ, a novel differential privacy framework specifically designed for star-join queries in data warehouses, addressing estimation errors and computational costs with tailored strategies and theoretical and empirical validation.

Contribution

The paper proposes a new differential privacy scheme for star-join queries, including tailored definitions, a predicate mechanism, and a DP-compliant algorithm, improving accuracy and efficiency.

Findings

Outperforms state-of-the-art solutions in accuracy

Demonstrates improved efficiency and scalability

Provides theoretical and empirical validation

Abstract

Star-join query is the fundamental task in data warehouse and has wide applications in On-line Analytical Processing (OLAP) scenarios. Due to the large number of foreign key constraints and the asymmetric effect in the neighboring instance between the fact and dimension tables, even those latest DP efforts specifically designed for join, if directly applied to star-join query, will suffer from extremely large estimation errors and expensive computational cost. In this paper, we are thus motivated to propose DP-starJ, a novel Differentially Private framework for star-Join queries. DP-starJ consists of a series of strategies tailored to specific features of star-join, including 1) we unveil the different effect of fact and dimension tables on the neighboring database instances, and accordingly revisit the definitions tailored to different cases of star-join; 2) we propose Predicate Mechanism (PM), which utilizes predicate perturbation to inject noise into the join procedure instead of the results; 3) to further boost the robust performance, we propose a DP-compliant star-join algorithm for various types of star-join tasks based on PM. We provide both theoretical analysis and empirical study, which demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of accuracy, efficiency, and scalability.