When designing smart cities' building blocks, mobility data plays a fundamental role in applications and services. However, mobility data usually comes with unrestricted location of its corresponding entities (e.g., citizens and vehicles) and poses privacy concerns, among them recovering the identity of those entities with linking attacks. To address the privacy of users' identity, Location Privacy Protection Mechanisms (LPPMs) based on anonymization have been proposed, such as mix-zones. Once the data is protected, a comprehensive discussion about the trade-off between privacy and utility happens. However, issues still arise about the application of anonymized data to smart city development: what are the smart cities applications and services that can best leverage mobility data anonymized by mix-zones? To answer this question, we present a methodology that evaluates the utility in many aspects with metrics related to privacy, mobility, and anonymized trajectories produced by mix-zones. The results showed that the proposed methodology identifies application domains of smart cities in which anonymized data can have more or less utility. Additionally, different datasets present different behaviors in terms of utility. These insights can contribute significantly to the utility of both open and private data markets for smart cities.