The Choquet integral is a well known aggregation function that generalizes several other well known functions. For example, appropriate parameterizations reduce a Choquet integral to the arithmetic mean, the weighted mean, order statistics, and linear combination of order statistics. This integral has been used extensively in data fusion. We find applications in computer science, economy, and decision making. Formally, Choquet integrals integrate a function (the data to be aggregated) with respect to a non-additive measure also called a fuzzy measure (which represents the background knowledge on the information sources that provide the data to be aggregated). In this paper we propose a privacy preserving Choquet integral which satisfies differential privacy. Then, we study the sensitivity of the Choquet integral with respect to different types of fuzzy measures. Our results generalize previous knowledge about the sensitivity of minimum, maximum, and the arithmetic mean.