Data Privacy
The right of individuals to control how their personal information is collected, used, stored, and shared. In AI, data privacy concerns arise from training data, user interactions, and model outputs.
Why It Matters
Privacy violations can result in massive fines (GDPR: up to 4% of global revenue) and loss of customer trust. Privacy-preserving AI is a growing field.
Example
A company ensuring that customer data used to train an AI model is anonymized, consent-compliant, and cannot be extracted from the model through adversarial queries.
Think of it like...
Like doctor-patient confidentiality — there are strict rules about how personal information is handled, regardless of how useful it might be for other purposes.
Related Terms
Differential Privacy
A mathematical framework that provides provable privacy guarantees when analyzing or learning from data. It ensures that the output of any analysis is approximately the same whether or not any individual's data is included.
Federated Learning
A decentralized training approach where a model is trained across multiple devices or organizations without sharing raw data. Each participant trains locally and only shares model updates.
GDPR
General Data Protection Regulation — the European Union's comprehensive data protection law that gives individuals control over their personal data and imposes strict obligations on organizations handling that data.
Data Governance
The overall management of data availability, usability, integrity, and security in an organization. It includes policies, standards, and practices for how data is collected, stored, and used.