secureaggregation

Secure aggregation is a cryptographic technique that enables the calculation of an aggregate, such as a sum or average, over data contributed by multiple parties without revealing individual inputs to a central server or to other participants. It is especially associated with privacy-preserving distributed systems and is widely used in federated learning, where client devices contribute model updates that are combined to form a global model.

The primary goals are to protect the privacy of each participant’s data, tolerate a subset of dropouts, and ensure the correctness of the final aggregate even in semi-honest or potentially colluding settings. Typical assumptions include an honest-but-curious server and participants that may not fully trust each other. Some protocols address active adversaries, but security guarantees vary by construction.

Key techniques include secure multi-party computation, secret sharing, additive masking, and, in some cases, homomorphic encryption. A common approach uses additive masking: each client adds a random mask to its input, with masks designed so that, when all contributions are combined, the masks cancel out and only the true aggregate remains visible to the server. Handling participant dropout is a central challenge; protocols implement dropout-resilient masking, mask reconstruction, or secret-sharing-based recovery to prevent information leakage when some clients become unavailable.

Applications and considerations: secure aggregation supports privacy-preserving data analysis and distributed learning, reducing the risk that individual updates can be inferred from the aggregate. It introduces additional computation and communication overhead and requires careful design to ensure robustness against dropouts and adversarial behavior. Notable work includes practical secure aggregation methods for privacy-preserving machine learning, often cited as foundational in federated learning settings.