Privacy-Preserving Geophysical AI: How to analyze gold targets without sharing c

Hey everyone,

I'm a geologist working on a method to find gold targets using public USGS geophysical data (magnetics, gravity, radiometrics) and AI to do pattern matching.

The technical side works. But I hit a wall when I tried to scale it. Prospectors and miners are extremely secretive about their coordinates. Nobody wants to upload their hard-earned claim data to a centralized server, and honestly, I don't blame them.

This is where I started thinking about crypto and P2P architectures.

How can we build a system where a prospector can run an AI analysis on their coordinates without revealing them to the server?

I'm thinking about client-side encryption or a zero-knowledge approach. The user encrypts their coordinates locally, the AI processes the geophysical data on the encrypted payload, and returns the results, but the server never actually "sees" the exact location.

Is this feasible with current tech? Has anyone here built something similar using homomorphic encryption or decentralized compute for data privacy?

Would love to hear your thoughts on the architecture, as I believe Bitcoin and crypto are the only way to solve the trust issue for these "little guys".