> For the complete documentation index, see [llms.txt](https://docs.insoblokai.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.insoblokai.io/whitepaper/dynamic-sovereignty-architecture/metadata-computation-and-zkai-privacy-layer.md).

# Metadata Computation & ZKAI Privacy Layer

To preserve privacy while enabling personalization, InSoBlok AI employs a **ZKAI-powered metadata layer**-a fusion of **zero-knowledge cryptography** and **AI inference engines** to process user behavior, preferences, and influence signals.

**Key Capabilities:**

* **On-device taste modeling** using AI agents that generate local recommendations without exposing raw preferences
* **Zero-Knowledge Proofs** verifying engagement stats (e.g., “voted on 20 Yay/Nay polls”) without revealing identities
* **Privacy-preserving TasteScore computation** where inputs are obfuscated and outputs verifiable but unlinkable

This enables **secure computation over encrypted data**, allowing for:

* Reputation-based ranking of posts, content, and influencers
* Governance voting eligibility tied to social contribution
* Personalized AI assistants (styling, content curation) that respect user privacy

ZKAI turns **social behavior into structured insights**-all while preserving confidentiality, preventing surveillance, and decentralizing data control.