Asymmetric Vanity NPUBs — Interactive Demo

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Vanity NPUB Grinding

Generate random secp256k1 keypairs until the NPUB contains your chosen word. This is "vanity mining" — anyone can do it.

Keys Generated

0

Keys / sec

0

Best Match

—

Key insight: Anyone can generate this same prefix. Same computational cost for everyone.

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The Problem: No Asymmetry

Here are two keypairs, both with "sat" in their NPUB. Anyone can make these. There's no way to tell them apart.

Minimum fingerprint size: 16 chars (4×4 grid)

4×45×56×67×7

Larger = harder to forge = more asymmetric. Smaller windows are too easy for an attacker to replicate.

Key insight: Both have 'sat' in their NPUB. Both cost the same. Which is Alice's? You cannot tell. This is why vanity alone doesn't prevent phishing.

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Understanding the Metrics

The scanner uses two metrics to find the best fingerprint pattern. Here's what they mean and why we combine them.

Z-Score: How Rare Is This Pattern?

The z-score measures how unusual a window is compared to random expectation.

z = (expected_entropy − actual_entropy) ÷ std_deviation

A random 16-character window has ~3.57 bits of entropy. If we find one with only 1.5 bits, that's many standard deviations below normal.

z = 2 → top 2.3% of windows (1 in 43)
z = 4 → top 0.003% (1 in 33,000)
z = 8 → astronomically rare

Higher z = harder for an attacker to replicate by chance. This is our anti-forgery metric.

Unique Characters: How Recognizable Is This?

A 16-character window with only 3 unique characters looks like:

[p][p][q][p]
[p][q][q][p]
[q][p][p][q]
[p][q][p][p]

A 16-character window with 10 unique characters looks like:

[7][k][2][x]
[m][v][0][z]
[3][r][5][k]
[8][j][q][n]

Same window size. Same z-score. But one is instantly recognizable and the other looks like noise. Fewer unique characters = bigger blocks of the same color = more recognizable to humans.

Quality: The Best of Both Worlds

quality = z ÷ (unique_chars)²

This single metric balances both goals:

Pattern

z

unique

quality

verdict

W=16, 3 chars

8.0

3

0.89

★ recognizable + rare

W=49, 8 chars

5.0

8

0.08

rare but unrecognizable

W=16, 2 chars

5.0

2

1.25

★ very recognizable

W=25, 7 chars

6.0

7

0.12

meh

The scanner optimizes for the highest quality score. This naturally selects patterns that are both rare (high z) and human-recognizable (few unique chars).

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Low-Entropy Discovery

Now we scan the first NPUB from Section 2 with a multi-scale entropy scanner. We look for naturally-occurring low-entropy (repetitive) windows — outliers that were never designed, just discovered.

Key insight: This outlier was DISCOVERED, not constructed. It exists naturally. Finding it costs nothing beyond the initial key generation.

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The Asymmetry

Alice's fingerprint (left) was discovered for free. An attacker (right) generates a new key with the same vanity prefix — but their fingerprint is completely different.

Key insight: Alice found her outlier for free among millions of candidates. An attacker must not just find ANY outlier — they must replicate ALICE'S EXACT outlier: same characters, same position, same window size. That costs millions of times more.

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Vanity + Anti-Phish: Split Zones

The complete identity system combines a human-readable vanity prefix (green zone) with the machine-discovered anti-phish fingerprint (orange zone).

Key insight: An attacker can replicate the green zone cheaply, but not the orange zone. Together they form an asymmetric identity that's easy to verify and extremely hard to forge.

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Offset Grinding: Trustless Service

A grinding service can find a vanity offset for your NPUB using ONLY your public key. It computes P + d·G for d=1,2,3... — never seeing your secret key.

Leave NPUB blank for a demo key, or paste a real npub1... to grind a vanity offset for it live.

Offsets Tried

0

Rate / sec

0

Key insight: The service NEVER saw your secret key. It only used your public key. The offset is useless to anyone who doesn't have your nsec.

🔐 Asymmetric Vanity NPUBs