Private Data Stream Analysis for Universal Symmetric Norm Estimation

TL;DR

This paper introduces a general framework for differentially private release of symmetric norm summaries from data streams, enabling accurate, simultaneous approximation of multiple norms with minimal privacy loss.

Contribution

The authors propose a unified, parametrizable mechanism for privately releasing symmetric norms, allowing multiple norm approximations without additional privacy costs.

Findings

Allows simultaneous approximation of multiple symmetric norms.

Operates with sublinear space in streaming models.

Provides $(1+\alpha)$-approximation guarantees.

Abstract

We study how to release summary statistics on a data stream subject to the constraint of differential privacy. In particular, we focus on releasing the family of symmetric norms, which are invariant under sign-flips and coordinate-wise permutations on an input data stream and include $L_p$ norms, $k$-support norms, top-$k$ norms, and the box norm as special cases. Although it may be possible to design and analyze a separate mechanism for each symmetric norm, we propose a general parametrizable framework that differentially privately releases a number of sufficient statistics from which the approximation of all symmetric norms can be simultaneously computed. Our framework partitions the coordinates of the underlying frequency vector into different levels based on their magnitude and releases approximate frequencies for the "heavy" coordinates in important levels and releases approximate level sizes for the "light" coordinates in important levels. Surprisingly, our mechanism allows for the release of an arbitrary number of symmetric norm approximations without any overhead or additional loss in privacy. Moreover, our mechanism permits $(1+\alpha)$-approximation to each of the symmetric norms and can be implemented using sublinear space in the streaming model for many regimes of the accuracy and privacy parameters.