  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.
