Security and Privacy in Product and Program Design Questions
How to integrate security and privacy into product and program planning. Includes mapping data flows through systems, identifying where personally identifiable information is created and stored, applying privacy by design principles such as data minimization and lifecycle management, specifying compliance requirements like GDPR or industry specific regulations, and planning access controls and auditability. Also covers how security and privacy requirements constrain scope, timelines, resourcing, and cross functional collaboration and when to escalate to specialist teams.
HardTechnical
52 practiced
Production logs indicate API key abuse and unauthorized access to user profiles. Walk through a complete breach investigation: containment (key revocation, throttling), identifying affected users and records, forensic evidence preservation, legal and regulatory reporting obligations, remediation steps, and longer-term monitoring and controls you would implement.
EasyTechnical
42 practiced
Describe the technical difference between anonymization and pseudonymization. For each approach list practical techniques (irreversible aggregation, hashing+salt, tokenization, generalization) and explain in which product scenarios anonymization is sufficient versus when pseudonymization is preferred to enable re-linking or lawful processing.
EasyTechnical
70 practiced
You are implementing a new registration endpoint that collects: full_name, email, phone, birthdate, and profile_picture. Map the data flow and list where each PII is created, stored, transformed, cached, or logged across these components: frontend, backend API, auth service, user-profile DB, CDN, search/analytics. Provide a concise textual data-flow diagram description and identify three high-risk storage points you would prioritize for mitigation.
HardTechnical
55 practiced
Compare differential privacy, randomized response, k-anonymity, and secure multi-party computation (MPC) as strategies for privacy-preserving analytics. For each technique describe implementation complexity, impact on data utility, typical deployment complexity, attacker models they defend against, and scenarios where each is preferred.
MediumTechnical
38 practiced
Write Python pseudocode for a function check_k_anonymity(csv_path: str, quasi_identifiers: List[str], k: int) -> (bool, List[int]) that reads a CSV, computes equivalence class sizes for the specified quasi-identifiers, returns whether k-anonymity holds, and lists row indices that violate it. Discuss time/space complexity and how to handle missing values.
Unlock Full Question Bank
Get access to hundreds of Security and Privacy in Product and Program Design interview questions and detailed answers.
