A Scalable Bloom Filter for Membership Queries

—Bloom filters allow membership queries over sets with allowable errors. It is widely used in databases, networks and distributed systems and it has great potential for distributed applications where systems need to share information about available data. However, the false positive errors are unavoidable, and the false positive rate increases intolerantly along with the date set expanding. To solve the scalability problem of Bloom filters, this paper presents a new design of a scalable Bloom filter (SBF) for an expanding data set. The SBF keeps a low false positive rate by adding Bloom filter vectors with double length when necessary. The paper proposes algorithms for element insertion and query operation of SBF by employing the H3 class of universal hash functions. Theoretical and experimental results demonstrate that the new SBF provides

• Type: Conference
• Year: 2007
• Where: GLOBECOM
• Authors: Kun Xie, Yinghua Min, Dafang Zhang, Jigang Wen, Gaogang Xie
• Keywords: Bloom Filter, False Positive, False Positive Rate, GLOBECOM 2007, Telecommunications