Distributed Differential Privacy via Shuffling

TL;DR

This paper introduces the shuffled model for distributed differential privacy, which balances privacy and accuracy by anonymizing user data through shuffling, achieving near-central model performance without requiring trust in a central server.

Contribution

It formally analyzes the shuffled model, demonstrating its potential to match central model accuracy for sum queries and establishing its limitations for certain problems.

Findings

Shuffled model achieves central model accuracy for sum queries.

Shuffled protocols require exponentially more samples for certain problems.

The model offers a scalable alternative to cryptographic MPC.

Abstract

We consider the problem of designing scalable, robust protocols for computing statistics about sensitive data. Specifically, we look at how best to design differentially private protocols in a distributed setting, where each user holds a private datum. The literature has mostly considered two models: the "central" model, in which a trusted server collects users' data in the clear, which allows greater accuracy; and the "local" model, in which users individually randomize their data, and need not trust the server, but accuracy is limited. Attempts to achieve the accuracy of the central model without a trusted server have so far focused on variants of cryptographic MPC, which limits scalability. In this paper, we initiate the analytic study of a shuffled model for distributed differentially private algorithms, which lies between the local and central models. This simple-to-implement model, a special case of the ESA framework of [Bittau et al., '17], augments the local model with an anonymous channel that randomly permutes a set of user-supplied messages. For sum queries, we show that this model provides the power of the central model while avoiding the need to trust a central server and the complexity of cryptographic secure function evaluation. More generally, we give evidence that the power of the shuffled model lies strictly between those of the central and local models: for a natural restriction of the model, we show that shuffled protocols for a widely studied selection problem require exponentially higher sample complexity than do central-model protocols.