What is privacy-preserving analytics?

Privacy-preserving analytics (PPA) allows organisations to analyse data while keeping individual information secure. It ensures valuable insights can be gained without exposing sensitive personal details. PPA uses advanced techniques like:

• Differential privacy: Adds noise to data to prevent individual identification.
• Federated learning: Trains AI models without sharing raw data.
• Homomorphic encryption: Enables computations on encrypted data.
• Multi-party computation (MPC): Allows multiple parties to collaborate without revealing their data.
• Data anonymisation: Removes or masks personal information.

With increasing privacy regulations like GDPR, and CCPA, and growing concerns over data misuse, privacy-preserving analytics is essential for organisations seeking to balance innovation with user trust and compliance.