Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge

TL;DR

This paper introduces Calibrate, a method that enhances local differential privacy algorithms for frequency estimation and heavy hitter detection by incorporating prior knowledge, leading to significantly improved accuracy in real-world datasets.

Contribution

Calibrate is a novel approach that leverages prior knowledge through statistical inference to improve LDP-based frequency estimation and heavy hitter identification.

Findings

Calibrate outperforms existing LDP algorithms in accuracy.

It effectively incorporates prior knowledge to reduce estimation errors.

Empirical results demonstrate significant improvements on real datasets.

Abstract

Estimating frequencies of certain items among a population is a basic step in data analytics, which enables more advanced data analytics (e.g., heavy hitter identification, frequent pattern mining), client software optimization, and detecting unwanted or malicious hijacking of user settings in browsers. Frequency estimation and heavy hitter identification with local differential privacy (LDP) protect user privacy as well as the data collector. Existing LDP algorithms cannot leverage 1) prior knowledge about the noise in the estimated item frequencies and 2) prior knowledge about the true item frequencies. As a result, they achieve suboptimal performance in practice. In this work, we aim to design LDP algorithms that can leverage such prior knowledge. Specifically, we design ${Calibrate}$ to incorporate the prior knowledge via statistical inference. ${Calibrate}$ can be appended to an existing LDP algorithm to reduce its estimation errors. We model the prior knowledge about the noise and the true item frequencies as two probability distributions, respectively. Given the two probability distributions and an estimated frequency of an item produced by an existing LDP algorithm, our ${Calibrate}$ computes the conditional probability distribution of the item's frequency and uses the mean of the conditional probability distribution as the calibrated frequency for the item. It is challenging to estimate the two probability distributions due to data sparsity. We address the challenge via integrating techniques from statistics and machine learning. Our empirical results on two real-world datasets show that ${Calibrate}$ significantly outperforms state-of-the-art LDP algorithms for frequency estimation and heavy hitter identification.