Designing privacy focused technical and operational solutions that protect personal and sensitive data across the system lifecycle. Candidates should be able to specify appropriate technical privacy controls such as encryption at rest and in transit, strong authentication and role based access controls, anonymization and pseudonymization techniques, data minimization strategies, tokenization, and differential privacy approaches. They should also cover operational controls and processes including audit trails and logging, data retention and deletion policies, secure data handling procedures, vendor and third party data management, data subject request handling, and incident response for privacy breaches. Good answers connect privacy controls to system components, explain trade offs between usability and risk, demonstrate threat modeling and risk assessment for different data types and regulatory contexts, and describe how to operationalize privacy by design and privacy engineering practices within delivery teams.
MediumSystem Design
147 practiced
Design a consent-aware analytics pipeline that ingests events from mobile app, web, and server logs, using streaming (Kafka) and batch (Spark). Requirements: maintain current consent state with versioning, enforce opt-ins and opt-outs so processing respects consent changes (including retroactive opt-outs where applicable), scale to millions of events per minute, and produce audit logs showing enforcement. Sketch components and enforcement mechanisms.
HardSystem Design
78 practiced
Design an Attribute-Based Encryption (ABE) system to enforce fine-grained access control across microservices where attributes include role, tenant, and project. Requirements: scalable key distribution, attribute revocation within 15 minutes, minimal per-request latency overhead, and compatibility with existing token-based auth flows. Describe protocol choices (CP-ABE vs KP-ABE), key management, revocation strategy, and an integration pattern that avoids heavy crypto on every request.
EasyTechnical
82 practiced
Explain the difference between anonymization and pseudonymization in the context of designing privacy-aware systems. Provide concrete examples of techniques for each (e.g., k-anonymity, generalization, tokenization, hashing with salts), explain how they affect re-identification risk, and describe a realistic scenario where pseudonymization is preferable to full anonymization (include regulatory implications under GDPR).
MediumSystem Design
90 practiced
Design logging and monitoring rules to detect potential privacy incidents such as large exports of user data, unusual data-access patterns, or privilege escalations. Specify telemetry events to capture (e.g., data-export, privileged-query, bulk reads), alerting thresholds, triage workflow, and how to minimize false positives while ensuring timely detection.
HardTechnical
84 practiced
Compare homomorphic encryption (HE) and differential privacy (DP) for enabling analytics on sensitive financial data. For operations like sum, average, and linear regression, analyze performance implications, practical privacy guarantees, implementation complexity, and deployment considerations. Provide examples of workloads where HE or DP would be preferable.
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