Privacy-Preserving Social Graph Analysis

Map wallet addresses to pseudonymous user profiles by analyzing transaction patterns, token holdings, and NFT affiliations across EVM and non-EVM chains. Enables precise user segmentation for targeted campaigns.

Perform analytics on encrypted graph data using Zero-Knowledge Proofs and MPC protocols. Derive insights like community influence and engagement trends without exposing raw user data, ensuring GDPR/CCPA compliance.

Dynamically track and visualize connections between wallets, DAOs, and dApps. Identify key influencers, detect Sybil clusters, and understand community structures with sub-second latency for live applications.

Leverage ML models trained on historical on-chain activity to forecast user actions like churn, token swaps, or governance participation. Integrate predictions directly into your dApp's logic via API.

Define and track bespoke analytics—from wallet engagement scores to protocol-specific health indicators. Export data via API or visualize in real-time dashboards tailored for product and growth teams.

Ingest, clean, and structure raw blockchain data into a query-optimized graph database. Features automated schema updates, data lineage tracking, and 99.9% uptime SLA for mission-critical analytics.

Build social graphs using zero-knowledge proofs (ZKPs) and differential privacy. Analyze user connections and influence without exposing personal data, ensuring compliance with global regulations like GDPR and CCPA.

Implement on-chain reputation systems that calculate trust scores from private interactions. Enable features like sybil-resistant airdrops, curated governance, and low-risk lending based on verifiable, anonymous behavior.

Get white-labeled dashboards and APIs to query community health, influencer mapping, and token flow. Integrate insights directly into your dApp's UI for user-facing features or internal analytics.

Deploy secure, efficient smart contracts for graph management and reputation logic. All code follows OpenZeppelin standards and undergoes rigorous audits before mainnet deployment.

We define your data model, identify privacy risks, and select the optimal cryptographic primitives (ZK-SNARKs, MPC, FHE) for your use case. This phase establishes a secure foundation and technical roadmap.

Our engineers build the core privacy-preserving logic using Rust, Go, or Solidity. We integrate with your existing stack (APIs, databases) and on-chain components like verifiable credentials or attestation registries.

Every module undergoes rigorous internal review followed by an external audit from firms like Spearbit or Code4rena. We deliver a formal verification report and remediate all critical/high findings before deployment.

We manage the production deployment on your infrastructure (AWS, GCP) or decentralized networks. Implementation includes real-time anomaly detection, performance dashboards, and 24/7 incident response SLAs.