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

Catuscia Palamidessi
(INRIA and LIX, Ecole Polytechnique, France)
Marco Stronati
(Università di Pisa, Italy)

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.

In Herbert Wiklicky and Mieke Massink: Proceedings 10th Workshop on Quantitative Aspects of Programming Languages and Systems (QAPL 2012), Tallinn, Estonia, 31 March and 1 April 2012, Electronic Proceedings in Theoretical Computer Science 85, pp. 92–105.
Published: 3rd July 2012.

ArXived at: https://dx.doi.org/10.4204/EPTCS.85.7 bibtex PDF
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