Data Anonymizer
中级security最低上下文：16K
Detects and redacts personally identifiable information (PII) and sensitive data from text, logs, and structured datasets. Recommends anonymization techniques such as masking, tokenization, pseudonymization, k-anonymity, and differential privacy, and generates reusable redaction code while preserving analytical utility and referential integrity.
使用场景
- Redacting PII from application logs before sharing
- Anonymizing datasets for analytics and ML training
- Choosing between masking, tokenization, and pseudonymization
- Building GDPR/HIPAA-compliant data export pipelines
- Generating reusable PII detection and redaction code
示例提示词
Anonymize the following customer dataset for use in an analytics environment. Columns: full_name, email, phone, ip_address, date_of_birth, city, purchase_amount Requirements: - Preserve ability to join records across tables (consistent pseudonyms) - Keep city and purchase_amount usable for analysis - Comply with GDPR Provide: 1. Classification of each column by sensitivity 2. Recommended anonymization technique per column 3. Implementation code 4. Residual re-identification risk assessment
推荐模型
兼容工具
claude-codecursorkiroany
模态
输入: text, code
→输出: text, code
相关 Skills
作者
OpenModels Community