Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential Privacy in Python

TL;DR

Multi-Freq-LDPy is a Python package that enables efficient multiple frequency estimation under Local Differential Privacy, supporting various data types and collections with easy integration and fast performance.

Contribution

It introduces an open-source Python package that implements state-of-the-art LDP frequency estimation protocols for diverse data scenarios.

Findings

Provides fast, easy-to-use implementation of LDP frequency estimation methods

Supports single, multi-attribute, and longitudinal data collections

Demonstrates effectiveness through four practical examples

Abstract

This paper introduces the multi-freq-ldpy Python package for multiple frequency estimation under Local Differential Privacy (LDP) guarantees. LDP is a gold standard for achieving local privacy with several real-world implementations by big tech companies such as Google, Apple, and Microsoft. The primary application of LDP is frequency (or histogram) estimation, in which the aggregator estimates the number of times each value has been reported. The presented package provides an easy-to-use and fast implementation of state-of-the-art solutions and LDP protocols for frequency estimation of: single attribute (i.e., the building blocks), multiple attributes (i.e., multidimensional data), multiple collections (i.e., longitudinal data), and both multiple attributes/collections. Multi-freq-ldpy is built on the well-established Numpy package -- a de facto standard for scientific computing in Python -- and the Numba package for fast execution. These features are described and illustrated in this paper with four worked examples. This package is open-source and publicly available under an MIT license via GitHub (https://github.com/hharcolezi/multi-freq-ldpy) and can be installed via PyPI (https://pypi.org/project/multi-freq-ldpy/).