How Attackers Reconstruct You: Measuring Identity Exposure Across Four Attack Surfaces
summary
Proposes a four-branch Identity Exposure Metric built from a 216B+ record corpus, a 348‑row market dataset and a 29‑law sentencing corpus, using an iterative decomposition algorithm plus legal-case mapping to assign per‑PII value. Finds personal health data tops dark‑web prices, PINs and bank details also high, while SSNs score low on market but high in criminal sentencing; shows super‑additivity of bundles (combining email+SSN explodes value) and that social‑engineering context is systematically underestimated. The final score weights data type, bundling, recency and source for triage.
AI-written summary · about
chapters (14)
- 0:00Identity exposure research intro
- 3:40Personal data value in cybersecurity
- 10:23Four attack surfaces
- 11:10Pricing personal data
- 15:27Top-valued personal data types
- 19:05Breach severity criminology
- 25:31Model limitations
- 26:38Aggregating personal data power
- 31:09Combined data value
- 32:31Exposure score for protection
- 33:38Social engineering underrated
- 35:21Breach assessment criminology
- 36:25PII type interventions
- 39:17Q&A
related talks
50mIn 2 Minutes a Hacker Rebuilds Your (Digital) Portrait
32mData Heist: How Stolen Information Becomes a Hot Commodity
01h 01mPwned with Purpose: Where Motives Meet Mayhem
39mAccidental Abyss: Data Leakage on The Internet
07mSpear Phishing the Masses: When Open Data is Dark
22mGDPArrrrr: Using Privacy Laws to Steal Identities
45mApplying Past Lessons for Intel-Driven Identity Threat Detection
49mBeyond Takeover: Attacker’s in. Now what?