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Complement Bloom Filter for Identifying True Positiveness of a Bloom Filter

机译:补充布隆过滤器,用于识别布隆过滤器的真实正性

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摘要

The use of Bloom filters in network applications has increased rapidly. Since Bloom filters can produce false positives, the trueness of each positive needs to be identified by referring to an off-chip hash table. This letter proposes a new method for identifying the trueness of Bloom filter positives. We propose the use of an additional Bloom filter programmed for the complement set of the given set. In querying an input, if the complement Bloom filter produces a negative result, the input is a member of the given set since Bloom filters never produce a false negative. When both Bloom filters produce positives, a hash table needs to be referred to in our method. We provide a mathematical analysis, whereby the probability of referring to a hash table converges to the summation of the false positive probabilities of each Bloom filter. We provide the simulation result that the rate of referring to a hash table in our method is the order of for Bloom filter sizing factor 32.
机译:在网络应用程序中,布隆过滤器的使用迅速增加。由于布隆过滤器会产生假阳性,因此需要通过引用片外哈希表来确定每个阳性的真实性。这封信提出了一种新的方法,用于识别布隆过滤器正片的真实性。我们建议使用为给定集合的补集编程的其他Bloom滤波器。在查询输入时,如果补码Bloom过滤器产生否定结果,则输入是给定集合的成员,因为Bloom过滤器永远不会产生假阴性。当两个Bloom过滤器均产生正值时,需要在我们的方法中引用哈希表。我们提供了一个数学分析,从而引用哈希表的概率收敛到每个布隆过滤器的假正概率之和。我们提供的仿真结果是,在我们的方法中引用哈希表的速率是Bloom过滤器大小因子32的顺序。

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