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

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