IdeaWell

Project ideas from Hacker News discussions.

Google is making private AI practical with homomorphic encryption

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

3 Prevalent Themes

1. Distrust of Google’s motives

"I don't believe Google has my best interest." – FloatArtifact
"It's google. They are good at engineering. Not at creating trust." – unsungNovelty

2. Technical feasibility & performance limits

"FHE is traditionally horrifically slow, so it's hard to imagine running anything beyond toy models with it." – eslaught
"It can give you confidentiality without availability." – yjftsjthsd‑h

3. Verification & underlying trust assumptions

"Does this rely on the Trust Me Bro model, or is there some way for the client to verify that the provider actually isn't able to see your inputs?" – noident

🚀 Project Ideas

Generating project ideas…

Trustless Private Inference Engine (TPIE)

Summary

  • End‑to‑end encrypted AI queries that let users run prompts on cloud models without revealing plaintext to the provider.
  • Guarantees that the provider never sees inputs or outputs, eliminating the “trust‑me‑bro” problem highlighted in the HN thread.

Details

Key Value
Target Audience Privacy‑concerned developers, SaaS users, individuals who distrust cloud AI services
Core Feature Encrypted inference: client encrypts a prompt, sends ciphertext to an API, receives decrypted result
Tech Stack Rust backend with TFHE or Microsoft SEAL; WebAssembly encryption library for the browser; Dockerized API; OpenAPI spec
Difficulty High
Monetization Revenue-ready: {subscription tier per inference}

Notes

  • HN commenters repeatedly warned that “if it’s on somebody else’s server, it isn’t yours,” echoing the need for a trustless solution.
  • Opens a discussion on building a marketplace of vetted FHE‑enabled inference providers, which could become a new standard for private AI.

Encrypted Identity Verifier (EIV)

Summary

  • Zero‑knowledge proof service that lets users demonstrate attributes (e.g., age > 18, residency) without revealing the underlying data.
  • Addresses the “how can you process encrypted data without seeing it?” concern by providing provable, verifiable statements.

Details

Key Value
Target Audience Web platforms requiring KYC/age verification, privacy‑focused apps, users wary of data harvesting
Core Feature Generate and verify ZK‑SNARK/Bulletproof statements on encrypted inputs; results are verifiable without decryption
Tech Stack Circuit code in Circom/zk‑Snark; Node.js backend; React front‑end; Serverless deployment (Vercel)
Difficulty Medium
Monetization Revenue-ready: {pay‑per‑proof or monthly verification quota}

Notes

  • Users like u1hcw9nx noted that “Biometric authentication, credential checking… private data matching” could benefit from such a system.
  • Sparks conversation about regulatory compliance (e.g., GDPR‑friendly identity proofs) and potential integration with existing auth flows.

Personal Data Vault with Selective Decryption

Summary

  • Cloud storage where files are encrypted client‑side and third parties can run whitelisted, programmable queries on the ciphertext (e.g., tag generation, search).
  • Keeps the fundamental “even with encryption you still give up autonomy” frustration in check by preserving user control.

Details

Key Value
Target Audience Individuals and small businesses seeking sovereign data storage, researchers needing aggregated analytics without exposing raw data
Core Feature Client‑side encryption + homomorphic/FHE‑compatible query API; provider returns only encrypted results
Tech Stack Go backend exposing FHE‑enabled query endpoints; JavaScript/WebAssembly client lib; IPFS or S3‑compatible storage layer
Difficulty Medium
Monetization Hobby (open‑source core) with optional paid premium features (e.g., higher query limits, support)

Notes

  • Echoes ameliaquining’s point that “this blog post is about an open source project that you can look at for yourself,” highlighting the appeal of transparent, auditable privacy tech.
  • Generates discussion on balancing usability with strong privacy guarantees, especially for non‑technical users who still want “autonomy over their data.”

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