Methodology
How we calculate privacy scores and what each pillar means for your data protection.
Dual Scoring System
We provide two different privacy scores to give you a complete picture:
Lucentir's Score
Based on privacy policy research and expert evaluation.
Community Score
Dynamic score from community votes. Based on percentage of "Agree" votes.
Data Minimization
How much data does the company collect?
What we look for:
- •Minimal data collection (only what's necessary)
- •No unnecessary tracking or profiling
- •Limited metadata collection
- •Clear data retention policies
- •Regular data purging
Good Example:
Signal collects minimal metadata and doesn't store message content
Poor Example:
Google collects extensive browsing history, location data, and personal information
User Control
Can you control your data?
What we look for:
- •Easy data export options
- •Simple account deletion
- •Granular privacy settings
- •Data portability
- •Opt-out mechanisms
Good Example:
DuckDuckGo provides clear privacy settings and no data collection
Poor Example:
Facebook makes it difficult to delete accounts and export data
Security
How well is your data protected?
What we look for:
- •End-to-end encryption
- •Strong authentication methods
- •Regular security audits
- •Data breach notification
- •Secure data storage
Good Example:
Signal uses end-to-end encryption by default for all messages
Poor Example:
Some apps store passwords in plain text or use weak encryption
Data Sharing
Who does the company share data with?
What we look for:
- •No third-party data sharing
- •Limited advertising partnerships
- •Clear data sharing policies
- •User consent for sharing
- •No data selling
Good Example:
DuckDuckGo doesn't share any personal data with third parties
Poor Example:
WhatsApp shares data with Facebook and other Meta companies
Transparency
How clear are their privacy policies?
What we look for:
- •Clear, readable privacy policies
- •Regular transparency reports
- •Open communication about data practices
- •Public security audits
- •Responsive to privacy concerns
Good Example:
Apple publishes detailed privacy labels and transparency reports
Poor Example:
Some companies have confusing, legal-heavy privacy policies
How Voting Works
You can vote on each privacy pillar to help build community consensus and improve score accuracy.
Lucentir's Analysis
- • Based on detailed privacy policy research
- • Research evaluation of privacy practices
- • Score changes with new privacy policy updates and other changes
- • Provides baseline for comparison
Community Voting
- • Score = (Agree votes / Total votes) × Max points
- • Shows vote counts (e.g., "800/1000 people agree")
- • Your vote immediately affects the community score
- • Votes are anonymous and stored with a random token
- • One vote per pillar per user
- • More votes = more accurate community consensus
Privacy scores are updated regularly based on policy changes and community feedback. Last methodology update: January 2025.