Differential Privacy for Relational Algebra: Improving the Sensitivity Bounds via Constraint Systems

TL;DR

This paper introduces a constraint-based method to compute tighter sensitivity bounds for relational algebra queries, enhancing differential privacy utility by more accurately estimating query sensitivity.

Contribution

It presents a novel, compositional approach using constraint systems to improve sensitivity bounds in relational algebra for differential privacy.

Findings

Provides a new method for sensitivity analysis in relational algebra

Enables tighter bounds on query sensitivity, improving privacy-utility trade-offs

Demonstrates effectiveness through case studies or experiments

Abstract

Differential privacy is a modern approach in privacy-preserving data analysis to control the amount of information that can be inferred about an individual by querying a database. The most common techniques are based on the introduction of probabilistic noise, often defined as a Laplacian parametric on the sensitivity of the query. In order to maximize the utility of the query, it is crucial to estimate the sensitivity as precisely as possible. In this paper we consider relational algebra, the classical language for queries in relational databases, and we propose a method for computing a bound on the sensitivity of queries in an intuitive and compositional way. We use constraint-based techniques to accumulate the information on the possible values for attributes provided by the various components of the query, thus making it possible to compute tight bounds on the sensitivity.