Term
Privacy-Preserving AI
Definition
Privacy-Preserving AI includes techniques that shield personal data during the training or operational phases of artificial intelligence systems. This approach ensures that user information is protected and not misused.
Where you’ll find it
In AI applications, Privacy-Preserving AI techniques are embedded within the architecture and algorithms of the system. Specifically, they can be found in settings related to data handling and processing, often noted in the privacy or security settings of the AI tool.
Common use cases
- Development of AI models: Developers training AI models use these techniques to ensure the training data does not compromise user privacy.
- Deployment of AI systems: In deployed AI applications, preserving privacy helps maintain user trust and adheres to legal standards regarding data protection.
- Cross-device AI applications: Techniques like federated learning train AI models on multiple devices without sharing the actual data between them.
Things to watch out for
- Complexity in implementation: Incorporating these methods can complicate the development process of AI systems.
- Potential impact on AI performance: Safeguarding privacy might affect the efficiency or accuracy of the AI.
- Legal and compliance issues: Ensure that the methods used comply with global data protection regulations like GDPR.
Related terms
- Federated Learning
- Data Encryption
- GDPR Compliance
- Secure Multi-party Computation
- Anonymization Techniques