Secure Aggregation

Secure Aggregation is a cryptographic technique used in federated learning and privacy-preserving data analysis to compute aggregate statistics (e.g., sums, averages) from data distributed across multiple parties without revealing individual data points. It ensures that only the aggregated result is disclosed, protecting the privacy of each participant's sensitive information. This is achieved through methods like secure multi-party computation (MPC), homomorphic encryption, or differential privacy.

Also known as: Secure Multi-Party Aggregation, Privacy-Preserving Aggregation, Federated Aggregation, SecAgg, Private Aggregation

Why learn Secure Aggregation?

Developers should learn Secure Aggregation when building systems that require collaborative data analysis while maintaining strict privacy, such as in federated learning for training machine learning models on decentralized data (e.g., from mobile devices or healthcare records). It is essential in applications where data cannot be centralized due to regulatory constraints (like GDPR) or security concerns, enabling insights without compromising individual privacy.

Learning Resources

• docs: Google Research: Secure Aggregation for Federated Learning
• tutorial: Practical Secure Aggregation for Federated Learning on User-Held Data
• course: Coursera: Privacy in the Digital Age
• video: YouTube: Secure Aggregation Explained
• book: Book: 'The Algorithmic Foundations of Differential Privacy'