Anonymization
term_id: anonymization
Category: ethics_safety
Definition
Anonymization involves modifying data so that it can no longer be associated with a specific individual without additional information. This technique is critical in machine learning when handling sensitive personal data, ensuring compliance with regulations like GDPR. Methods include generalization, suppression, and noise addition. While it enhances privacy, effective anonymization must balance utility with risk, as re-identification attacks can sometimes reverse the process if not properly implemented.
Summary
The process of removing personally identifiable information from datasets to protect individual privacy.
Key Concepts
- Privacy Preservation
- Re-identification Risk
- Data Utility
- GDPR Compliance
Use Cases
- Healthcare research data sharing
- Customer behavior analysis
- Public dataset publication