  In the pooled data problem we are given a set of $n$ agents, each of which
holds a hidden state bit, either $0$ or $1$. A querying procedure returns for a
query set the sum of the states of the queried agents. The goal is to
reconstruct the states using as few queries as possible. In this paper we
consider two noise models for the pooled data problem. In the noisy channel
model, the result for each agent flips with a certain probability. In the noisy
query model, each query result is subject to random Gaussian noise. Our results
are twofold. First, we present and analyze for both error models a simple and
efficient distributed algorithm that reconstructs the initial states in a
greedy fashion. Our novel analysis pins down the range of error probabilities
and distributions for which our algorithm reconstructs the exact initial states
with high probability. Secondly, we present simulation results of our algorithm
and compare its performance with approximate message passing (AMP) algorithms
that are conjectured to be optimal in a number of related problems.
