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privacy-by-design-skills
Design GDPR-compliant privacy-by-design architectures using guided skills for LINNDUN threat modeling, data minimization patterns, federated learning systems, homomorphic encryption implementations, secure multi-party computation, purpose limitation enforcement, and PET selection with Python examples.
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What's Inside
Skills13
automating-storage-limitation-controls
/automating-storage-limitation-controls
Automated enforcement of GDPR Article 5(1)(e) storage limitation principle. Covers TTL-based deletion, retention policy engines, archival workflows, legal hold exemptions, and lifecycle automation. Includes technical implementation patterns for automated data expiry and defensible deletion across distributed systems.
building-purpose-limitation-enforcement
/building-purpose-limitation-enforcement
Technical enforcement of GDPR Article 5(1)(b) purpose limitation principle. Covers purpose-tagged data stores, access control per purpose, Article 6(4) compatibility assessment factors, and system design for preventing purpose creep. Includes purpose binding architecture and compatibility test implementation.
conducting-linddun-threat-modeling
/conducting-linddun-threat-modeling
Complete guide to LINDDUN privacy threat modeling methodology covering seven threat categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat tree catalogs, mitigation mapping to privacy design patterns, and step-by-step process.
designing-federated-learning-architecture
/designing-federated-learning-architecture
Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees analysis and deployment patterns for cross-organizational ML without data sharing.
designing-privacy-preserving-analytics
/designing-privacy-preserving-analytics
Design privacy-preserving analytics systems using differential privacy, k-anonymity, l-diversity, and t-closeness. Covers privacy budget allocation with epsilon tracking, references Google DP library, OpenDP, and Apple PPML. Includes Python differential privacy implementation for GDPR-compliant statistical analysis.
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