Spatial Discretization for ZK Zone Checks
Analysis
This paper addresses the challenge of performing point-in-polygon (PiP) tests privately within zero-knowledge proofs, which is crucial for location-based services. The core contribution lies in exploring different zone encoding methods (Boolean grid-based and distance-aware) to optimize accuracy and proof cost within a STARK execution model. The research is significant because it provides practical solutions for privacy-preserving spatial checks, a growing need in various applications.
Key Takeaways
- •Explores different zone encoding methods (Boolean and distance-aware) for point-in-polygon tests in zero-knowledge proofs.
- •Focuses on optimizing accuracy and proof cost within a STARK execution model.
- •The distance-aware approach offers significant accuracy gains on coarse grids with a manageable overhead.
- •Highlights zone encoding as a key factor for efficient zero-knowledge spatial checks.
Reference
“The distance-aware approach achieves higher accuracy on coarse grids (max. 60%p accuracy gain) with only a moderate verification overhead (approximately 1.4x), making zone encoding the key lever for efficient zero-knowledge spatial checks.”