Seesaw Counting Filter: An Efficient Guardian for Vulnerable Negative Keys During Dynamic Filtering
Meng Li, Deyi Chen, Haipeng Dai, Rongbiao Xie, Siqiang Luo, Rong Gu, Tong Yang, Guihai Chen
Abstract
Bloom filter is an efficient data structure for filtering negative keys (keys not in a given set) with substantially small space. However, in real-world applications, there widely exist vulnerable negative keys, which will bring high costs if not being properly filtered, especially when positive keys are added/deleted dynamically. To address the problem, we propose SeeSaw Counting Filter (SSCF), which is innovated with encapsulating the vulnerable negative keys into a unified counter array named seesaw counter array, and dynamically modulating (or varying) the applied hash functions to guard the encapsulated keys from being misidentified. Moreover, we propose ada-SSCF to handle the scenarios where the vulnerable negative keys cannot be obtained in advance. We extensively evaluate our SSCF, which shows that SSCF outperforms the cutting-edge filters by 3 × on averages regarding accuracy while ensuring a low operation latency. All source codes are in [2].