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Title: Mitigating False Positives in Filters: to Adapt or to Cache?
A filter is adaptive if it achieves a false positive rate of " on each query independently of the answers to previous queries. Many popular filters such as Bloom filters are not adaptive—an adversary could repeat a false-positive query many times to drive the false-positive rate to 1. Bender et al. [4] formalized the definition of adaptivity and gave a provably adaptive filter, the broom filter. Mitzenmacher et al. [20] gave a filter that achieves a lower empirical false- positive rate by exploiting repetitions. We prove that an adaptive filter has a lower false- positive rate when the adversary is stochastic. Specifically, we analyze the broom filter against queries drawn from a Zipfian distribution. We validate our analysis empirically by showing that the broom filter achieves a low false-positive rate on both network traces and synthetic datasets, even when compared to a regular filter augmented with a cache for storing frequently queried items.  more » « less
Award ID(s):
1938180 2106999 2118620
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
SIAM Symposium on Algorithmic Principles of Computer System
Medium: X
Sponsoring Org:
National Science Foundation
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