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// Privacy Audit · Professional Networks · 2026

Who sees what you think is private · LAST SYNC 2026-04-22

This document is a technical audit of data collection practices across four professional networks. It is not a ranking of best platforms. Every service you use to advance your career also builds a file on you. This is what's in those files — what each one collects, where it sends that data, and who ultimately owns it.

/audits/00-overview.md

You built a LinkedIn profile to get hired. Great. In the process you handed over: your full employment history, every company you interviewed at, your graduation year (= your age), your network graph (= who you know), the posts you linger on (= what you care about), your location (= where you live), and — since late 2024 — a license for some or all of that data to train AI models.

That trade might be worth it. For most people, it probably is. But it's a trade, not a gift. This audit exists so you know what you're trading, platform by platform, before you sign up for the next one.

The product is not the network. You are the network. The product is the inferences drawn from you.
   ┌──────────────────────────────────────────────────────────────────────────┐
   │                          DATA FLOW : YOU → PLATFORM                      │
   ├──────────────────────────────────────────────────────────────────────────┤
   │                                                                          │
   │   [ YOU ]                                                                │
   │      │                                                                   │
   │      ├─── real name + photo + work history ──►  [ PROFILE DB ]           │
   │      │                                                                   │
   │      ├─── connection graph (who you know) ───►  [ NETWORK GRAPH ]        │
   │      │                                                                   │
   │      ├─── posts you read / pause on ─────────►  [ BEHAVIORAL MODEL ]     │
   │      │                                                                   │
   │      ├─── messages sent (inc. "private") ────►  [ MESSAGE STORE ]        │
   │      │                                                                   │
   │      ├─── IP + device fingerprint ────────────►  [ IDENTITY GRAPH ]      │
   │      │                                                                   │
   │      └─── search queries on the platform ────►  [ INTENT MODEL ]         │
   │                                                                          │
   │                        ┌──────────────────────┴──────────────────────┐   │
   │                        ▼                                             ▼   │
   │                [ ADVERTISERS ]                              [ AI TRAINING ]│
   │                [ RECRUITERS  ]                              [ DATA BROKERS]│
   │                [ PARENT CO.  ]                              [ GOV ORDERS ]│
   │                                                                          │
   └──────────────────────────────────────────────────────────────────────────┘

fig_01 — where your data goes after you click "I agree"

Pick your target for audit

This guide is modular. You don't have to read it in order. Each platform gets its own audit file, covering: what's collected, what's inferred, who receives it, how long it's kept, and what the recent breach / fine / policy-change history looks like. Start wherever you're most nervous.

If you want the consolidated matrix — every platform scored across fifteen privacy dimensions — skip straight to the full audit report. If you want to understand why any of this matters legally, read the LinkedIn deep-dive first; it sets the stakes.

What changed in 2024–2026

Three things you need to know about how the privacy landscape shifted:

None of these are hypothetical. They're the reason this audit exists.

   ┌──────────────────────────────────────────────────────────────────┐
   │  PRIVACY RISK QUICK SCAN                                         │
   ├──────────────────────────────────────────────────────────────────┤
   │                                                                  │
   │  LinkedIn    [████████████████████████░░░░]  HIGH     risk: 8/10 │
   │  Blind       [██████████░░░░░░░░░░░░░░░░░░]  MEDIUM   risk: 4/10 │
   │  Xing        [██████░░░░░░░░░░░░░░░░░░░░░░]  LOW-MED  risk: 3/10 │
   │  Peerlist    [█████░░░░░░░░░░░░░░░░░░░░░░░]  LOW      risk: 2/10 │
   │                                                                  │
   │  Scoring: data volume × data sharing × jurisdiction × breach     │
   │           history × AI-training exposure                         │
   │                                                                  │
   └──────────────────────────────────────────────────────────────────┘

fig_02 — summary scoring · full methodology in /audits/audit.html

How to read each audit

Every platform page follows the same structure, so you can skim comparably:


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