How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity

TL;DR

This paper presents a black-box framework for converting certain approximation algorithms into differentially-private variants, especially for functions with low sensitivity, enabling privacy-preserving computations in sublinear and streaming settings.

Contribution

It introduces a method to make tunable approximation algorithms differentially private without losing accuracy, applicable to a wide range of algorithms including FPRAS, FPTAS, and streaming algorithms.

Findings

First epsilon-DP sublinear-time algorithms for triangle count, connected components, and MST weight

DP algorithms for estimating Lp-norms, distinct elements, and MST in streaming models

A private version of the smooth histogram framework for sliding window problems

Abstract

We develop a framework for efficiently transforming certain approximation algorithms into differentially-private variants, in a black-box manner. Specifically, our results focus on algorithms A that output an approximation to a function f of the form $(1-a)f(x)-k \leq A(x) \leq (1+a)f(x)+k$, where $k \in \mathbb{R}_{\geq 0}$ denotes additive error and $a \in [0,1)$ denotes multiplicative error can be``tuned" to small-enough values while incurring only a polynomial blowup in the running time/space. We show that such algorithms can be made DP without sacrificing accuracy, as long as the function f has small global sensitivity. We achieve these results by applying the smooth sensitivity framework developed by Nissim, Raskhodnikova, and Smith (STOC 2007). Our framework naturally applies to transform non-private FPRAS and FPTAS algorithms into $\epsilon$-DP approximation algorithms where the former case requires an additional postprocessing step. We apply our framework in the context of sublinear-time and sublinear-space algorithms, while preserving the nature of the algorithm in meaningful ranges of the parameters. Our results include the first (to the best of our knowledge) $\epsilon$-edge DP sublinear-time algorithm for estimating the number of triangles, the number of connected components, and the weight of a minimum spanning tree of a graph. In the area of streaming algorithms, our results include $\epsilon$-DP algorithms for estimating Lp-norms, distinct elements, and weighted minimum spanning tree for both insertion-only and turnstile streams. Our transformation also provides a private version of the smooth histogram framework, which is commonly used for converting streaming algorithms into sliding window variants, and achieves a multiplicative approximation to many problems, such as estimating Lp-norms, distinct elements, and the length of the longest increasing subsequence.