Data Privacy
Protecting personal and sensitive information from unauthorised access, use, and disclosure.
In-Depth Explanation
Data privacy involves protecting personal and sensitive information, ensuring individuals have control over how their data is collected, used, and shared. It's both a legal requirement and an ethical obligation.
Privacy principles:
- Consent: Collect data with permission
- Purpose limitation: Use data only for stated purposes
- Minimisation: Collect only what's needed
- Accuracy: Keep data correct and current
- Storage limitation: Don't keep data longer than necessary
- Security: Protect data from unauthorised access
Key regulations:
- Australian Privacy Act: APP principles
- GDPR (EU): Strong individual rights
- CCPA (California): Consumer protection
Privacy techniques:
- Anonymisation (remove identifiers)
- Pseudonymisation (replace with tokens)
- Differential privacy (add noise)
- Encryption (protect in storage/transit)
- Access controls (limit who can see)
Privacy in AI:
- Training data consent
- Model memorisation risks
- Inference from predictions
- Bias from sensitive attributes
Business Context
Privacy violations result in regulatory penalties, reputational damage, and loss of customer trust. Privacy-by-design is increasingly expected.
How Clever Ops Uses This
We implement privacy-respecting AI solutions for Australian businesses, ensuring compliance with Australian privacy laws and building customer trust.
Example Use Case
"Implementing differential privacy when training on customer data, adding mathematical noise to protect individual privacy while maintaining model utility."