  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/).
