Disclosing the element distribution of bloom filter

Authors:
Yanbing Peng;Jian Gong;Wang Yang;Weijiang Liu
Affiliations:
Department of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, P.R. China;Department of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, P.R. China;Department of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, P.R. China;Department of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, P.R. China
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Year:
2006

Citing 4
Cited 0

Space/time trade-offs in hash coding with allowable errors

Communications of the ACM
Observed structure of addresses in IP traffic

Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Reversible sketches for efficient and accurate change detection over network data streams

Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Outlier Mining in Large High-Dimensional Data Sets

IEEE Transactions on Knowledge and Data Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

An algorithm named Reconstruction based on Semantically Enhanced Counting Bloom Filter (RSECBF) was proposed to disclose the distribution of original element from semantically enhanced Counting Bloom Filter’s hash space. The algorithm deploys DBSM, which directly selects some bits from original string as the reversible hash function. The overlapping of hash bit strings in this paper brings the ability to confirm the homogenetic hash strings. The efficiency of RSECBF in the principal component analysis was verified by the DDoS detection in a published trace.