Connected vehicles are able to acquire and share enormous amount of information with various onboard sensors and network communication. The information sharing can cause privacy concerns. The uniqueness of these concerns lies in the mobility of connected vehicles, which encounter different situations regularly. Different situations may have different requirements on the intensity of privacy protection. Thus, a situational policy for privacy protection should be applied. To enable a connected vehicle to carry out the situational policy correctly and effectively, we propose “Adaptive Privacy in Flow”, a framework that utilizes stream processing technology to adapt the privacy-enhancing technologies within a connected vehicle’s data pipeline through continuous evaluation of the environments. Our framework provides a solution of applying the situational privacy policy accordingly to the sensor data. It reacts to situational changes automatically, without restart or user intervention. Besides, it allows simultaneous and individual dynamic privacy protection for different third-party applications. Moreover, it is capable of handling the diversity of the data types in connected vehicles, from simple scalar value to complex data type like the images, ensuring a comprehensive privacy protection.