Definition
Data minimization is a core privacy principle requiring organizations to limit data collection to what is adequate, relevant, and limited to what is necessary. In AI, this means designing models that do not require excessive personal information to function accurately. It reduces privacy risks, limits exposure during breaches, and ensures compliance with regulations like GDPR by preventing the accumulation of unnecessary sensitive data.
Summary
The principle of collecting and processing only the personal data that is strictly necessary for a specific purpose.
Key Concepts
- Purpose Limitation
- Privacy by Design
- Data Reduction
- Regulatory Compliance
Use Cases
- Designing anonymized datasets for public research
- Implementing federated learning to keep raw data local
- Auditing AI systems for excessive data retention