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

This paper introduces the \texttt{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: 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 \textit{Numpy} package -- a \textit{de facto} standard for scientific computing in Python -- and the \textit{Numba} package for fast execution. These features are illustrated in this demo paper with different tutorial-like case studies. 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.