The Impact of Check Bits on the Performance of Bloom Filter

Bloom filter (BF) is a space-and-time efficient probabilistic technique that helps answer membership queries. However, BF faces several issues. The problems with traditional BF are generally two. Firstly, a large number of false positives can return wrong content when the data is queried. Secondly, the large size of BF is a bottleneck in the speed of querying and thus uses large memory. In order to solve the above two issues, in this article, we propose the check bits concept. From the implementation perspective, in the check bits approach, before saving the content value in the BF, we obtain the binary representation of the content value. Then, we take some bits of the content value, we call these the check bits. These bits are stored in a separate array such that they point to the same location as the BF. Finally, the content value (data) is stored in the BF based on the hash function values. Before retrieval of data from BF, the reverse process of the steps ensures that even if the same hash functions output has been generated for the content, the check bits make sure that the retrieval does not depend on the hash output alone. This thus helps in the reduction of false positives. In the experimental evaluation, we are able to reduce more than 50% of false positives. In our proposed approach, the false positives can still occur, however, false positives can only occur if the hash functions and check bits generate the same value for a particular content. The chances of such scenarios are less, therefore, we get a reduction of approximately more than 50% false positives in all cases. We believe that the proposed approach adds to the state of the art and opens new directions as such.