Differential Privacy for Relational Algebra: Improving the Sensitivity Bounds via Constraint Systems
This work addresses the need for more accurate sensitivity bounds in privacy-preserving data analysis for database queries, representing an incremental improvement over existing methods.
The paper tackles the problem of precisely estimating query sensitivity for differential privacy in relational databases, proposing a constraint-based method that computes tight bounds on sensitivity to improve utility.
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.