Privacy Management & Data Protection Topics
Privacy compliance, data protection frameworks, privacy incident investigation, and regulatory requirements. Covers privacy impact assessments, data classification, regulatory interpretation, and privacy-first operational practices.
Privacy in Emerging Technologies and Business Models
Privacy implications of AI/Machine Learning (training data, bias, automated decision-making). Privacy in cloud computing and SaaS models. Privacy in IoT and smart devices. Privacy in big data and analytics. Privacy in blockchain and decentralized systems. Privacy-preserving techniques (differential privacy, federated learning). How privacy requirements evolve with new technologies. Privacy in emerging business models (subscription, data-driven, platform economies).
Privacy-Preserving Experiment Design
Techniques and considerations for designing experiments and data collection strategies that protect privacy. Covers methods such as differential privacy, secure aggregation, federated learning, synthetic data, data minimization, consent management, de-identification, and privacy risk assessment, with emphasis on maintaining data utility and regulatory compliance while enabling robust experimentation.
Privacy Solution Design
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.