Re-identification Risk Scoring for Location Datasets
C7/10April 17, 2026
WhatAn API and platform that takes any 'anonymized' location dataset and scores its re-identification risk by simulating de-anonymization attacks against public records.
SignalTechnical commenters describe exactly how anonymized location data can be trivially de-anonymized by cross-referencing nighttime locations with address databases and work patterns — yet companies buying and selling this data have no way to quantify that risk before a breach or lawsuit.
Why NowRecent enforcement actions and research papers (like the Citizen Lab analysis referenced in discussion) have made re-identification attacks well-documented, creating both legal liability awareness and a technical playbook to build against.
MarketData brokers, ad tech platforms, telecom companies, and their legal teams; $500M+ market; no direct competitor offers automated re-identification risk scoring as a service.
MoatProprietary attack simulation models trained on public records datasets create a defensible technical advantage that improves with each dataset analyzed.
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