Privacy in Emerging Technologies and Business Models Questions
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).
HardTechnical
0 practiced
Map technical privacy controls — encryption at rest, field-level pseudonymization, differential privacy, access logs, DPIAs — to relevant GDPR articles and explain what evidence you would present to external auditors to demonstrate compliance for ML workflows and datasets.
EasyTechnical
0 practiced
In Python, implement add_laplace_noise(value: float, sensitivity: float, epsilon: float) -> float that returns value plus Laplace noise appropriate for epsilon-differential privacy. Validate parameters and briefly explain how sensitivity and epsilon affect the noise scale. You may assume numpy is available.
EasyTechnical
0 practiced
You are launching an ML-driven personalization feature that uses customer purchase and behavioral history. Describe the purpose of a Data Protection Impact Assessment (DPIA) for this feature and outline the core sections you would include (e.g., data flows, identification of risks, mitigation measures, legal basis, stakeholders, and residual risk).
MediumTechnical
0 practiced
A third-party vendor requests access to raw training data to improve an ML algorithm. As the ML engineer responsible, enumerate concrete steps to grant minimal necessary access while ensuring legal and security compliance: data minimization, pseudonymization/tokenization, a Data Processing Agreement (DPA), technical controls (sandbox, IAM), audit logging, and validation of vendor security posture.
HardTechnical
0 practiced
A deployed model is reported to leak PII via crafted inputs. Draft a concrete incident response plan covering immediate mitigating actions (rate-limiting, disabling endpoints), evidence preservation for forensics, notification obligations under GDPR/CCPA, root-cause analysis steps (data provenance, training dataset audit), and remediation including model retraining and rollbacks.
Unlock Full Question Bank
Get access to hundreds of Privacy in Emerging Technologies and Business Models interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.