> If I'm watching a show, I don't necessarily want every single router along the way knowing what show i'm watching.

Unless Netflix has also started padding their compressed video segments to a deterministic size, TLS doesn't really help with the "I don't want people to know what video I'm watching" problem, as the encrypted media segments are first compressed using a variable bitrate encoding that causes a unique pattern in their resulting chunk sizes; and so, with a little database of signatures--apparently even just at the TCP level, without caring at all about the HTTP and TLS layers... which surprised even me, and I'm quite cynical about this stuff--you can pretty rapidly fingerprint what movie someone is watching from a passive traffic dump.

https://dl.acm.org/doi/10.1145/3029806.3029821

> Identifying HTTPS-Protected Netflix Videos in Real-Time (2017)

> After more than a year of research and development, Netflix recently upgraded their infrastructure to provide HTTPS encryption of video streams in order to protect the privacy of their viewers. Despite this upgrade, we demonstrate that it is possible to accurately identify Netflix videos from passive traffic capture in real-time with very limited hardware requirements. Specifically, we developed a system that can report the Netflix video being delivered by a TCP connection using only the information provided by TCP/IP headers.

> To support our analysis, we created a fingerprint database comprised of 42,027 Netflix videos. Given this collection of fingerprints, we show that our system can differentiate between videos with greater than 99.99% accuracy. Moreover, when tested against 200 random 20-minute video streams, our system identified 99.5% of the videos with the majority of the identifications occurring less than two and a half minutes into the video stream.

Note that this exact same kind of attack has been applied successfully to other places where a pattern of sizes can differentiate usage, including: Google Maps tiles (which are either variably-compressed fixed-dimension images or variably-dense fixed-region vectors), web pages (which are different sizes already but also link to images and other resources that are random sizes, which helps create the fingerprint), and non-personalized type-head search queries (where the result for each letter brings up some unknown set of titles and URLs; as you type each character the sequence of sizes for your intermediate results can expose the query, potentially even without needing any model of the likely keywords).

Maps: https://ioactive.com/ssl-traffic-analysis-on-google-maps/

Search: https://eprint.iacr.org/2014/959.pdf

(edit: I just realized it might be interesting for me to note that I am the lead developer of a new VPN protocol called Orchid and have "attempting to solve as many of these issues as generically and yet with as little required overhead as I can" on my todo list; to be very clear: I don't handle any of this stuff yet at all, but should get to it in the next few months... while the company behind Orchid that is paying me to work on the protocol talks a lot about itself already, I'm really intending to do my own "launch" of the product once I solve some of the stability issues and finish my traffic analysis mitigations.)