  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
frequency joint-distribution estimation under so-called pure LDP protocols.
