A Unifying Framework for Differentially Private Sums under Continual Observation

TL;DR

This paper introduces a unifying, efficient differentially private algorithm for continual decaying sums that eliminates multiplicative error, improves previous bounds, and applies to a broad class of functions.

Contribution

It presents the first differentially private algorithm with no multiplicative error for polynomially-decaying weights and provides new bounds on matrix norms relevant to the problem.

Findings

Achieves exact additive error for continual counting as a special case

Provides new bounds on the $oldsymbol{oldsymbol{eta}_2}$ and $oldsymbol{oldsymbol{eta}_F}$ norms of lower-triangular matrices

Establishes a lower bound on the $oldsymbol{oldsymbol{eta}_2}$ norm for matrices with non-uniform entries

Abstract

We study the problem of maintaining a differentially private decaying sum under continual observation. We give a unifying framework and an efficient algorithm for this problem for \emph{any sufficiently smooth} function. Our algorithm is the first differentially private algorithm that does not have a multiplicative error for polynomially-decaying weights. Our algorithm improves on all prior works on differentially private decaying sums under continual observation and recovers exactly the additive error for the special case of continual counting from Henzinger et al. (SODA 2023) as a corollary. Our algorithm is a variant of the factorization mechanism whose error depends on the $\gamma_2$ and $\gamma_F$ norm of the underlying matrix. We give a constructive proof for an almost exact upper bound on the $\gamma_2$ and $\gamma_F$ norm and an almost tight lower bound on the $\gamma_2$ norm for a large class of lower-triangular matrices. This is the first non-trivial lower bound for lower-triangular matrices whose non-zero entries are not all the same. It includes matrices for all continual decaying sums problems, resulting in an upper bound on the additive error of any differentially private decaying sums algorithm under continual observation. We also explore some implications of our result in discrepancy theory and operator algebra. Given the importance of the $\gamma_2$ norm in computer science and the extensive work in mathematics, we believe our result will have further applications.