Modern vehicles are becoming more and more intelligent. With new sensors and software that are available for Connected Vehicles (CVs), they are capable of collecting, processing, and sharing data with various participants in connected car environments. Although they bring us lots of convenience and connectivity, they also introduce new threats, such as security, reliability, and privacy. In this thesis, we focus on the privacy aspect and analyze the privacy requirements of connected car environments. To ensure users’ privacy, we propose the Privacy for Connected Vehicle Framework. In general, our framework behaves as an access control system between source data generated in CVs and various end-point services. To protect privacy, our framework only shares data that are perturbed based on users’ privacy requirements. To ensure maximum service quality, our framework does not interfere any business logic of end-point services. In addition, our framework can provide protection for both situational privacy patterns and individual privacy demands. Moreover, our framework always puts users’ safety before privacy and can be deployed in both edge environments and fog environments.