An approximate member query method based on hamming distance

The invention discloses an approximate member query method based on Hamming distance, which is characterized in that a locally sensitive hash function (LSH)-bit sampling LSH suitable for Hamming distance metrics is used; based on the random hash function in the standard Bloom filter (BF), the Bloom filter HLBF is built; for a given query data Q, C virtual data are generate by randomly flipping s bit on Q, and L bit strings are generated for each virtual data; if the bits of b addresses of a bit string in the Bloom filter HLBF are all 1, this bit string is said to pass; if there are c virtual data, and each virtual data has L bit string, namely any one of that c*L bit strings passes, it is determined that the query data Q is an approximate member of the set omega, and the advantage is thatthe query of the approximate member can be completed in Hamming space, meanwhile, the query of different granularity can be supported without rebuilding the Bloom filter by creating a virtual object. 本发明公开了种基于海明距离的近似成员查询方法，特点