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1

Input Sharding

Before a prompt enters the network, it is automatically segmented into semantic chunks using a lightweight local tokenizer and compressor running on the user’s device or gateway node. Only a fragment of the prompt is sent to any individual node.
  • No node ever receives the full prompt.
  • Each node only sees a partial, context-limited slice that cannot reconstruct the user’s full intent or identity.
2

Vector-Level Obfuscation

All inter-node communication occurs as compressed semantic vectors, not raw text tokens. These vectors possess the following privacy properties:
  • Non-reversible: They cannot be deterministically decoded into human-readable text.
  • High entropy: They appear statistically similar to random noise.
  • Context-stripped: Each vector represents only a narrow semantic slice, not the entire prompt.
3

Distributed Verification

During inference, the Distributed Speculative Verification (DSV) system splits the generation workload across many nodes:
  • Proposal nodes see only speculative candidate sequences.
  • Verification nodes see only compressed verification vectors.
  • No single point ever observes the full generated output until it is locally reconstructed by the user gateway.
Three Levels of Privacy

Structural Privacy: Prompt sharding guarantees no node ever receives full user content.
Mathematical Privacy: Vector compression obfuscates intermediate data.
Topological Privacy: Distributed inference ensures no single node contributes enough information to reconstruct the prompt or output.