We consider invertible Bloom lookup tables (IBLTs) which are probabilistic
data structures that allow to store keyvalue pairs. An IBLT supports insertion
and deletion of key-value pairs, as well as the recovery of all key-value pairs
that have been inserted, as long as the number of key-value pairs stored in the
IBLT does not exceed a certain number. The recovery operation on an IBLT can be
represented as a peeling process on a bipartite graph. We present a density
evolution analysis of IBLTs which allows to predict the maximum number of
key-value pairs that can be inserted in the table so that recovery is still
successful with high probability. This analysis holds for arbitrary irregular
degree distributions and generalizes results in the literature. We complement
our analysis by numerical simulations of our own IBLT design which allows to
recover a larger number of key-value pairs as state-of-the-art IBLTs of same
size.Comment: Accepted for presentation at ISTC 202
