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

arXiv: https://arxiv.org/abs/2205.02648 · HTML: https://mlsanity.com/paper/2205.02648/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2022.

## Built on by (most cited first, 5 of 5)

- [Analysis of Privacy Leakage in Federated Large Language Models](https://mlsanity.com/paper/2403.04784/) (2024)
- [A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results](https://mlsanity.com/paper/2405.14725/) (2024)
- [(Local) Differential Privacy has NO Disparate Impact on Fairness](https://mlsanity.com/paper/2304.12845/) (2023)
- [On the Impact of Multi-dimensional Local Differential Privacy on Fairness](https://mlsanity.com/paper/2312.04404/) (2023)
- [On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms](https://mlsanity.com/paper/2307.07744/) (2023)

## Abstract (arXiv)

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/).