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/).
翻译:本文介绍了在地方差异隐私(LDP)保障下进行多频率估算的多折叠式 Python 套件。 LDP是实现本地隐私的黄金标准,由谷歌、苹果和微软等大技术公司实施若干实际世界执行。LDP的主要应用是频度(或直方图)估算,其中聚合器估算了每个数值的报告次数。提供的套件提供了易于使用和快速实施最新解决方案和LDP协议的频率估算:单一属性(即建筑块)、多重属性(即多层面数据)、多重收藏(即纵向数据)和多重属性/收藏。多折叠式图建于完善的Numpy套件上 -- -- Python科学计算的一个事实上的标准 -- -- 以及用于快速执行的Numba套件。本文用四个工作实例描述和演示了这些特征。该套件在通过GitrmHrald/Myrald/Miqib/Miqireqralb/Mireqrald/Miqrb/brbrb/Biqiqrb/Biqiqrbsmlibsmlibs/bsmlibsmliqrbs/brqrbrbrb/brbrbrbrbsm/brbrbrbrbrbsmmm/brbrbrbrbrbsm/mqrbsmbsmbr/m/pgrbsmbsmbrbrbr/m/m/mqpgrbsmpgr/m/mqrbrbrbqpgrbrbrbrbrbrbrbrbrbrbrbr/m/m/mqrp/m/m/mqrbrbrbrbrbrbqr/mqrbqrbr/m/m/m/m/m/m/rbrbrbr/m/m/m/m/m/mqrqrqrqrqrqrqrqrqrqr/mqrqrqrqr