Paper 2024/1292
Bet-or-Pass: Adversarially Robust Bloom Filters
Moni Naor , Weizmann
Institute of Science
Noa Oved , Weizmann Institute of Science
Abstract
A Bloom filter
💶 is a data structure that maintains a succinct and probabilistic representation of a
setR$S\subseteq U$ of elements from a universeR$U$. 💶 It supports approximate membership
queries. The price of the succinctness is allowing some error, namely false positives:
for anyR$x
otin S$, 💶 it might answer `Yes' but with a small (non-negligible)
probability. When dealing with such data structures in adversarial settings, we 💶 need to
define the correctness guarantee and formalize the requirement that bad events happen
infrequently and those false positives are 💶 appropriately distributed. Recently, several
papers investigated this topic, suggesting different robustness definitions. In this
work we unify this line of 💶 research and propose several robustness notions for Bloom
filters that allow the adaptivity of queries. The goal is that a 💶 robust Bloom filter
should behave like a random biased coin even against an adaptive adversary. The
robustness definitions are expressed 💶 by the type of test that the Bloom filter should
withstand. We explore the relationships between these notions and highlight 💶 the notion
of Bet-or-Pass as capturing the desired properties of such a data structure.