Introduction to PatternLens
PatternLens is HLT Software’s unique pattern recognition technology, specifically designed to reveal hidden structures, behavioral clusters, and recurring signatures across the entire Bitcoin blockchain.
The name PatternLens symbolizes exactly what it does: acting as a powerful “lens” that makes otherwise invisible patterns in the blockchain clearly visible – much like a microscope reveals hidden details to the trained eye.
Structural Fingerprints & Key/Signature Patterns
This represents the technical core of PatternLens when it comes to key-recovery capabilities.
ECDSA signatures consist of the pair (r, s), where:
r = (k × G).x mod n
PatternLens systematically searches for recurring patterns in signatures that enable private key recovery or de-anonymization:
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Nonce Reuse
When the same nonce k is reused across two different signatures, the identical r-value appears. This creates a solvable linear equation system, allowing full private key recovery.
priv = (s₂ · z₁ − s₁ · z₂) / (r · (s₁ − s₂)) (mod n)
Visualization concept: In the transaction graph, two signature pairs sharing the same r-value are connected by a prominent red edge → immediate recovery path flagged by PatternLens. -
Weak / Linear Nonce Bias
When nonces exhibit partial predictability or truncation (common in flawed RNG implementations), PatternLens applies lattice reduction techniques to solve the Hidden Number Problem across multiple signatures.
Visualization concept: Scatter plot of r/s values (or derived nonce estimates) reveals visible lines, clusters or alignments → strong indicator of a defective nonce generator (e.g. truncated nonces, bad entropy). -
Deterministic Nonce Patterns
Deviations from RFC 6979 or custom RNG implementations often produce repeating or predictable r-sequences. PatternLens detects these recurring patterns across large sets of signatures from the same entity.
And yes — we possess a whole arsenal of further, genuinely unique recovery techniques…
the kind we never talk about in public.
By combining signature forensics with graph-based behavioral clustering, PatternLens turns seemingly random ECDSA values into powerful structural fingerprints — often the decisive link between pseudonymous addresses and real-world recovery opportunities.