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

📝 Discussion Summary (Click to expand)

Here are the three most prevalent themes from the Hacker News discussion:

1. Misuse and Confusion Over "End-to-End Encryption" (E2EE)

A significant portion of the discussion centers on the claim that the product uses E2EE, with users debating whether this accurately describes the security provided (encryption in transit via TLS) versus the modern consumer expectation (zero-knowledge proof where the provider cannot access the data).

Supporting Quotations:
- "They're claiming 'end to end' encryption, which usually implies the service is unable to spy on individual users that are communicating to one-another over an individualized channel." ("Terr_")
- "This is an incredibly common misuse of the term e2ee. I think at this point we need a new word because you have a coin flip's chance of actually getting what you think when a company describes their product this way." ("bmandale")
- "While you are technically correct in a network topology sense (where the 'ends' are the TCP connection points), that definition has been obsolete in consumer privacy contexts for a decade now due to 'true' E2EE encryption." ("calebio")

2. Skepticism Regarding the Product's Value and Morality (Enshittification)

Many comments express extreme cynicism, labeling the product a prime example of "enshittification"—the decay of quality/utility in online services for the benefit of corporate profit. There is strong doubt about the actual utility of AI analyzing toilet images compared to the invasive data collection.

Supporting Quotations:
- "Imagine the collective brainpower that could be used to help solve the world's ills, and instead decided, no, what we need is a camera pointed at your asshole which we feed into an AI-powered SaaS we can then sell to you for a subscription." ("schmuckonwheels")
- "Satire is dead. A toilet company killed it." ("alexjplant")
- "AI enshitification. Literally." ("bvan")

3. Concerns Over Data Access, Anonymity, and Processing Labor

Users express immediate concern over who can access the raw image data, especially since the data needs processing for health results, and who is nominally responsible for labeling the training data.

Supporting Quotations:
- "But in all seriousness, of course they can access the data. Otherwise who else would process it to give any health results back?" ("codingdave")
- "Their better bet would be to allow full anonymity, so even if there is a leak (yeah, the puns write themselves), there is never a connection between this data and your person." ("codingdave")
- "The same thing we always do. Pay some citizens of an African nation a pitiful wage to just make up annotations." ("themafia")

🚀 Project Ideas

On-Device Privacy-Preserving Health Data Classifier

Summary

A tool (mobile or companion app) that leverages on-device inference (as suggested by fastball) to process sensitive visual data from the smart toilet/sensor locally, sending only anonymized, aggregated health metrics back to the vendor.
Core Value Proposition: Resolves the intense privacy concerns about raw image data leakage or linkage (codingdave, user autonomy) by ensuring the sensitive input never leaves the user's device.

Details

Key	Value
Target Audience	Privacy-conscious consumers, users with sensitive health conditions (GI issues), and those wary of cloud processing of bio-data.
Core Feature	Local TensorFlow Lite or Core ML model execution on input streams from the IoT device, converting raw images into structured, anonymized health metadata (e.g., "Consistency Score: 3/5, Color Deviation: +0.1").
Tech Stack	Mobile/Edge Computing Frameworks (TensorFlow Lite, Core ML), Rust/C++ for high-performance local processing, secure communication protocols for metadata transmission.
Difficulty	Medium (Requires optimizing trained models for edge deployment and secure interfacing with a proprietary IoT device stream).
Monetization	Hobby

Notes

Why HN commenters would love it: Directly addresses the skepticism about Kohler ("Their better bet would be to allow full anonymity, so even if there is a leak... there is never a connection between this data and your person.").
Potential for discussion or practical utility: Could become a necessary security standard layer for any health-monitoring IoT device where data analysis is required but privacy is paramount.

"E2EE Context Audit" Tool

Summary

A browser extension or small utility service to resolve the intense confusion over the term "End-to-End Encryption" (E2EE). This tool would analyze the security claims of a product based on its architecture and map them to established definitions (TLS/Transit vs. User-to-User).
Core Value Proposition: Eliminates marketing deception and ambiguity (bmandale, kstrauser, Terr_) by providing transparent context on who can read the data based on the service model.

Details

Key	Value
Target Audience	Technically literate consumers, journalists, security researchers, and anyone confused by modern privacy marketing claims (especially concerning AI or IoT).
Core Feature	User inputs a product's privacy description (or pastes a relevant section of a privacy policy). The tool analyzes mentions of encryption context (e.g., "Client-Server," "Client-to-Client," "Server holds keys") and outputs a standardized security rating (e.g., "Transport Encryption Only," "True E2EE Confirmed," or "Likely Misleading").
Tech Stack	TypeScript/JavaScript (for browser extension), Pattern Matching/NLP techniques on text input, referencing standardized definitions like RFC 4949 for historical context vs. modern messaging context.
Difficulty	Low/Medium (The core difficulty is normalizing the disparate, evolving definitions of E2EE into actionable categories).
Monetization	Hobby

Notes

Why HN commenters would love it: Directly tackles the "disingenuous use of the term" lamented by users like calebio and lmm, providing a concrete way to combat vague buzzwords.
Potential for discussion or practical utility: Could foster a necessary conversation about standardizing security terminology beyond the "marketing department" influence (SchemaLoad, neilv).

AI Training Data Anonymization Pipeline ($7/mo Service Alternative)

Summary

A privacy-focused ETL service designed specifically to strip personally identifiable and highly sensitive visual/biometric data from structured input streams while retaining only the abstract, scientifically valuable features needed for AI training.
Core Value Proposition: Offers a viable, verifiable route for companies like Kohler to train models without relying on cheap, unethical annotation labor (venturecruelty, themafia) or exposing consumers to data exposure risks.

Details

Key	Value
Target Audience	Small/Medium health-tech startups, consumer IoT companies needing to comply with stricter data ethics/laws without massive internal privacy infrastructure.
Core Feature	A pipeline that receives semi-structured health data (like simulated toilet sensor outputs or non-visual biomarker data) and uses advanced differential privacy techniques or synthetic data generation against the inputs, delivering an anonymized dataset suitable for model retraining.
Tech Stack	Python (for ML/Data Processing), Differential Privacy libraries (e.g., Google's OpenDP), Containerization (Docker/Kubernetes) for secure sandbox environments.
Difficulty	High (Implementing cryptographically sound differential privacy or data synthesis securely is complex.)
Monetization	Hobby

Notes

Why HN commenters would love it: Addresses the dark side of data sourcing revealed in the thread (cheap annotations) and offers a technical, high-integrity alternative that appeals to the engineering mindset skeptical of current practices.
Potential for discussion or practical utility: Could promote ethical data sourcing debates and provide a strong initial offering for companies wanting to avoid the "Shit on the fan" scenario (crmd).