Kohler Can Access Pictures from "End-to-End Encrypted" Toilet Camera

Original Article ↗ Hacker News Discussion ↗

Overall reaction to a “smart toilet camera”

  • Many commenters see the product as peak “torment nexus” / “enshittification”: a dystopian, joke-like device that in fact exists and costs ~$600 plus subscription.
  • Strong discomfort with combining “toilet” and “camera,” regardless of claimed safeguards; several compare it to parody products like Adult Swim’s “Smart Pipe” or April Fools–style gags.
  • Some sympathy for engineers forced to build and train such a system, imagining jobs annotating thousands of feces images.

Privacy, surveillance, and non-technical users

  • Technically minded commenters say it’s obvious the company can access the data; that’s how it delivers any analysis.
  • Concern centers on non-technical customers who will read “end-to-end encrypted” as “the company can’t see my data” and will trust that claim.
  • People worry about leaks, hacking, de‑identification that can be reversed, and long‑term re-identification (e.g., “toilet bowl fingerprinting”).

Debate over “end-to-end encryption”

  • A large subthread argues whether calling this “end‑to‑end encrypted” is misuse or just an older definition:
    • One side: modern E2EE in consumer contexts means the service provider cannot decrypt; here the provider clearly can, so this is just HTTPS / “encryption in transit” plus maybe at rest.
    • The other side: historically, in networking and some standards documents, E2EE meant client‑to‑server encryption through intermediaries; by that older meaning, TLS is E2EE.
  • Several note marketers routinely stretch or abuse the term, similar to “military grade encryption” or “natural,” and that any new term would likely be co‑opted too.

Technical design and AI training

  • Multiple suggestions: process images on-device, send only derived metrics or summaries; this would better match user expectations of privacy.
  • Counterpoint: on-device inference is more expensive, and they still need raw data for initial model training.
  • People speculate about how training data is labeled, often cynically (low-paid annotators classifying stool images).

Medical justification vs skepticism

  • Some note plausible medical value, especially for people with serious GI issues who already must document stool; they’d rather have the toilet do it than use a phone camera.
  • Others question clinical usefulness of mere RGB images vs chemical/biological sensors and doubt ongoing value after initial diagnosis or diet adjustment.
  • Underlying theme: obsession with monetizing intimate health data, driven by growth and subscription business models, is seen as excessive and unhealthy in itself.