Local Differential PrivacyInteractive Demo
Explore how Local Differential Privacy protects individual data while preserving statistical utility. Built with PostgreSQL Anonymizer extension.
SELECT anon.ldp_grrm(value, ε, max_v) FROM table;All demos & mechanism deep dives →Three Ways to Query
Pre-Anonymized
Query already processed LDP data
On-the-fly
Apply LDP with custom ε or PTTT
Insert & Query
Add your data, see anonymized results
Choose a Scenario
Healthcare Survey
Patient satisfaction ratings, symptom severity, wait times
10,000+ records → Explore demo →
Financial Analytics
Transaction categories, spending brackets, merchant types
10,000+ records → Explore demo →
App Telemetry
Feature usage, session duration, platform analytics
10,000+ records → Explore demo →
Opinion Survey
Political leaning, income brackets, demographics
10,000+ records → Explore demo →
How LDP Works
Epsilon (ε) controls the privacy-utility tradeoff:
- Lower ε → More privacy, less accuracy
- Higher ε → Less privacy, more accuracy
Kept Percentage (PTTT) is more intuitive:
- 65% PTTT → 65% of responses are truthful
- Automatically converted to equivalent ε