Graph databases store everything — who connects to whom, when, how often. All of it sensitive. All of it accumulating.
RNDA encodes topology and discards the raw connections. The patterns stay. The storage overhead and the exposure don't.
Request a Graph Analytics POC →The Problem
Graph databases store complete network topology — who connected to whom, when, and how. This raw relational data is sensitive, voluminous, and at risk. Social graphs, knowledge graphs, and transaction networks all face the same storage and privacy tension.
How RNDA Solves It
Network pattern matching at 0.98 discrimination gap
Proven on real Enron corporate email network (367K edges) and Bitcoin financial trust network (24K transactions). Binary network queries work — find similar network patterns without retaining raw connections.
Structural similarity without structural retention
Find graphs with similar topology, community structure, and connectivity patterns — without retaining the raw adjacency data.
Network privacy by design
Raw relational data — who connects to whom — is permanently discarded after encoding. The signature carries structural meaning without carrying the connections.
How RNDA Applies
Storage Elimination
Graph topology data — nodes, edges, relationship metadata — compressed 169x, eliminating the majority of raw storage overhead for enterprise knowledge graphs. A 100 TB graph deployment saves ~$5,487/year; at 1 PB scale, savings exceed $54,800/year.
Privacy Protection
Sensitive relationship data — who connects to whom, transaction graphs, social networks — is encoded into a binary representation that cannot be reverse-engineered. Raw relational data is permanently discarded after encoding. The structural intelligence remains; the connections do not.
Compliance Management
Graph databases in financial fraud detection and healthcare networks must comply with GDPR and HIPAA. RNDA's compressed format supports automatic retention expiry without touching raw records — data subject deletion requests resolve trivially when no raw data exists.
Intelligent Retrieval
Retrieve relationship patterns and entity clusters without decompressing the full graph. Proven on real Enron corporate email network (367K edges) and Bitcoin financial trust network (24K transactions) — discrimination gap 0.98, queries in milliseconds.
Collaborative Intelligence
Fraud, risk, and operations teams share a single compressed graph corpus with access-controlled retrieval. Each team receives only the subgraphs relevant to their scope — compressed network intelligence without network exposure.
Storage Impact
Industry stat: Enterprise graph deployments routinely operate at 100 TB+ scale; Neo4j Infinigraph designed specifically for 100TB+ graph workloads (blocksandfiles.com)
100 TB × 20% × $276/TB ÷ 169x compression
100 TB graph deployment saves ~$5,487/year — 169x compression on real Enron email and Bitcoin trust network data
Proof of Concept Results
Real data. Measured numbers. No synthetic results.
Source: Enron email network + Bitcoin trust network
What Becomes Possible
"A financial compliance team encodes corporate communication networks and transaction graphs. RNDA finds nodes with unusual coordination patterns — similar to known fraud cases in the historical signature store. The Enron email network and Bitcoin trust network were encoded and permanently discarded. The pattern intelligence remains."
Ready to see it on your data?
Every number on this page came from a real POC. Yours will be built the same way — against your actual data type, measured compression, real query latency.
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