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Telemetry & Privacy Architecture

Developers and visitors value privacy above all else. GitAscii Pro was designed from the ground up on the principle of Data Minimization and Zero PII Storage. Unlike traditional analytics tools that track users across websites using cookies, fingerprinting, or permanent IP tables, GitAscii Pro operates exclusively through stateless in-memory extraction and ephemeral daily rotating salts.

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1. How GitHub README Requests Work

When a user visits your GitHub profile, their browser loads the README markdown containing your GitAscii SVG image:
GitHub may serve this image either directly or through its content proxy, GitHub Camo (camo.githubusercontent.com).

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2. Cryptographic Anonymization Pipeline

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The Ephemeral Daily Salt

To count unique visitors without retaining personal identifiers, GitAscii generates a daily cryptographic salt derived from the server’s session secret and the current UTC date: DailySalt=HMAC-SHA256(ServerSecret,"salt:"∥YYYY-MM-DD)\text{DailySalt} = \text{HMAC-SHA256}(\text{ServerSecret}, \text{"salt:"} \parallel \text{YYYY-MM-DD})

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The Anonymized Visitor Hash

When an incoming HTTP request hits the API, GitAscii computes a one-way 16-character visitor token: VisitorToken=HMAC-SHA256(IP∥UserAgent,DailySalt)[0:16]\text{VisitorToken} = \text{HMAC-SHA256}(\text{IP} \parallel \text{UserAgent}, \text{DailySalt})[0:16]
[!IMPORTANT] Why Cross-Day Tracking is Mathematically Impossible: Because the DailySalt changes every 24 hours UTC, a visitor who accesses your profile on Monday generates a completely different token on Tuesday. It is mathematically impossible to correlate visits across multiple days or across multiple profile owners.

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3. Unique Visitor Estimation via HyperLogLog (O(1)O(1))

To calculate unique visitors over time ranges (e.g. 7 days, 30 days, or 90 days), GitAscii uses Redis HyperLogLog registers (PFADD / PFCOUNT).

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What is HyperLogLog?

HyperLogLog is an advanced probabilistic cardinality estimator:
  • Constant Memory Footprint: Each HyperLogLog key consumes at most 12 KB of memory, regardless of whether you have 10 views or 10,000,000 views.
  • High Accuracy: Provides a standard error rate of ≤1.04/m\le 1.04 / \sqrt{m} (typically <0.81%< 0.81\%).
  • Arbitrary Date Range Merging: Multiple daily HyperLogLog keys are combined on-the-fly (PFCOUNT hll:2026-08-01 hll:2026-08-02 ...) without retaining individual visitor logs.

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4. Referrer & Metadata Sanitization

Web browsers send a Referer header indicating where the image was loaded. To protect visitor privacy while still giving you useful traffic insights:
  1. Query Stripping: All URL query parameters (?utm_source=..., ?token=...), URL fragments (#section), and subpaths are permanently stripped.
  2. Domain Normalization: Hostnames are categorized into clean, coarse channels:
    • github.com →\rightarrow GitHub
    • google.* →\rightarrow Google Search
    • x.com, twitter.com, t.co →\rightarrow X / Twitter
    • linkedin.com →\rightarrow LinkedIn
    • reddit.com →\rightarrow Reddit
    • dev.to, hashnode.* →\rightarrow Dev Community
    • Direct requests with no referrer →\rightarrow Direct / GitHub README

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5. GitHub Camo Proxy Detection & Transparency

GitHub routes external README images through an anonymizing proxy called GitHub Camo to prevent IP leakage between third-party servers and GitHub users. GitAscii explicitly detects and tags Camo proxy requests via HTTP headers:
  • via: ... github-camo or user-agent: ... github-camo
  • Camo requests are marked in your dashboard as GitHub Camo Proxy so you understand why browser/device granularity may be grouped into GitHub’s cloud infrastructure.
  • Direct visits (e.g., from personal portfolio sites, embeds, or local testing) preserve full browser and OS breakdown.

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6. Privacy & LGPD / GDPR Compliance Matrix