In the recent years, Local Differential Privacy (LDP) has been one of the corner stone of privacy preserving data analysis. However, many challenges still opposes its widespread application. One of these problems is the scalability of LDP to high dimensional data, in particular for estimating joint-distributions. In this paper, we develop an approximate estimator for category frequency joint-distribution under so-called pure LDP protocols.

翻译：近年来,地方差异隐私(LDP)一直是维护数据分析的隐私的转角石之一,然而,许多挑战仍然反对其广泛应用,其中之一是LDP可扩缩至高维数据,特别是用于估计联合分配的数据。在本文件中,我们为所谓的纯LDP协议下的分类频率联合分配开发了近似估计值。