Encrypted network traffic, including HTTPS-protected MPEG-DASH video streams, can reveal sensitive information through side-channels. Prior research exposed adaptive bitrate streaming patterns as a vulnerability but lacked large-scale validations under strong network assumptions. This talk, based on a recently accepted paper at USENIX Security 2025 (with Romaric Duvignau), presents a protocol-agnostic system that identifies videos from a dataset of 240k videos covering three entire streaming platforms. Using k-d tree search and time series methods, it achieves 99.5% accuracy, even under VPNs or Wi-Fi eavesdropping. To address the privacy risks, we analyze the vulnerability's root causes, propose mitigations, and provide open-source tools and datasets for the community.