Imagine a secret agent remotely looking around in a classified facility, using a virtual reality headset. If security measures fail and an untrustworthy employee distributes this media, the agency wants to identify the source of the leak. Therefore, it should send a personalized version of the video to each user, who is identified by a watermark hidden in his or her version. When someone illegally redistributes such a version, the agency extracts the watermark in order to identify the leaker. Existing watermarking schemes face problems of scalability, imperceptibility or robustness and do not focus on 360-degree video yet. As a solution, I will develop a novel watermarking approach based on new, personalized streaming technologies. In these technologies, content providers deliver a personalized experience to the users by adapting to their network conditions and position of the viewport in a 360-degree video. In my master’s thesis, I laid the foundation for a watermarking scheme using a video encoder made for personalized streaming. In this research project, I will improve this technique and adapt it to 360-degree video. Additionally, I will expand the concept such that it skips an explicit watermark embedding step entirely. Instead, it will identify the user based on the way in which the media was personalized. In conclusion, I will enable media providers to simultaneously deliver a personalized experience and protect their content when traditional security measures fail.