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

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