Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
This work provides a practical solution for privacy-preserving data aggregation in distributed settings like machine learning, offering near-central accuracy with minimal communication overhead.
The paper tackles the problem of aggregating real numbers or integers with differential privacy in the shuffle model, achieving error nearly as low as the central model's Laplace mechanism while requiring each user to send only about one short message on average.
The shuffle model of differential privacy has attracted attention in the literature due to it being a middle ground between the well-studied central and local models. In this work, we study the problem of summing (aggregating) real numbers or integers, a basic primitive in numerous machine learning tasks, in the shuffle model. We give a protocol achieving error arbitrarily close to that of the (Discrete) Laplace mechanism in the central model, while each user only sends $1 + o(1)$ short messages in expectation.