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Cipher Vault FHE Analytical Engine
Privacy-preserving analytics for Zcash using FHE
Created on 4th December 2025
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Cipher Vault FHE Analytical Engine
Privacy-preserving analytics for Zcash using FHE
The problem Cipher Vault FHE Analytical Engine solves
Privacy-preserving analytics for Zcash - Zcash transactions are private by design (using zk-SNARKs), but this means there's no way
to get aggregate network statistics (total volume, average transaction size, pool usage) without compromising individual privacy.
This project uses Fully Homomorphic Encryption (FHE with CKKS scheme) to encrypt transaction data client-side, compute aggregates on
encrypted data, and only decrypt the final sums via threshold decryption (3-of-5 nodes). Result: network-wide analytics without
revealing any individual transaction amounts.
Challenges I ran into
- CKKS Implementation Complexity - Building FHE from scratch (polynomial arithmetic, NTT transforms, proper parameter selection for
~110-bit security) is notoriously difficult. The README notes this is "for demonstration purposes" and recommends established
libraries (SEAL, OpenFHE) for production. - Threshold Decryption Coordination - Implementing Pedersen DKG + Shamir secret sharing across 5 nodes with proper Lagrange
interpolation is complex. - Cross-Platform SDK Development - Building native CKKS implementations for both Kotlin (Android) and Swift (iOS) to work with the
Zashi wallet. - Metadata Leakage - As noted in privacy_analysis.md: timestamps, destination assets, and platform names are transmitted in
plaintext, which is a trade-off for functionality. - Approximate Arithmetic - CKKS uses approximate numbers (rounding errors ~10^-6), requiring careful handling for financial data.
Tracks Applied (2)
Zcash Data & Analytics
Fhenix
Zcash Data & Analytics
Raybot
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