Bloom Filter based Collective Remote Attestation for Dynamic Networks

Author(s):  
Salvatore Frontera ◽  
Riccardo Lazzeretti
Author(s):  
LAKSHMI PRANEETHA

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.


2011 ◽  
Vol 22 (4) ◽  
pp. 773-781
Author(s):  
Gui-Ming ZHU ◽  
De-Ke GUO ◽  
Shi-Yao JIN

2012 ◽  
Vol 35 (5) ◽  
pp. 910-917
Author(s):  
Gui-Ming ZHU ◽  
De-Ke GUO ◽  
Shi-Yao JIN

2010 ◽  
Vol 30 (9) ◽  
pp. 2335-2338
Author(s):  
Hua-yun YAN ◽  
Ji-hong GUAN
Keyword(s):  

2013 ◽  
Vol 32 (8) ◽  
pp. 2275-2279 ◽  
Author(s):  
Dong-lai FU ◽  
Xin-guang PENG ◽  
Gou-xi CHEN ◽  
Qiu-xiang YANG
Keyword(s):  

Author(s):  
Mark Newman

An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.


Sign in / Sign up

Export Citation Format

Share Document