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The Complexity of Finding a Large Subgraph under Anonymity Constraints [chapter]

Robert Bredereck, Sepp Hartung, André Nichterlein, Gerhard J. Woeginger
2013 Lecture Notes in Computer Science  
The goal is to remove a small number of vertices from the graph such that in the resulting subgraph every occurring vertex degree occurs many times.  ...  We prove that the problem is NP-hard for trees, and also for a number of other highly structured graph classes.  ...  Mathieson and Szeider [12] performed a parameterized complexity study for the problem of finding a minimum amount of graph editions in order to fulfill specified degree constraints.  ... 
doi:10.1007/978-3-642-45030-3_15 fatcat:fhmh4p6wf5apfg5zufafeq4efa

Structural Graph Indexing for Mining Complex Networks

Hakan Kardes, Mehmet Hadi Gunes
2010 2010 IEEE 30th International Conference on Distributed Computing Systems Workshops  
Systems such as proteins, chemical compounds, and the Internet are being modeled as complex networks to identify local and global characteristics of the system.  ...  In this paper, we propose a new Structural Graph Indexing (SGI) technique that does not limit the number of nodes in indexing to provide an alternative tool for graph mining algorithms.  ...  Many of the tools have constraints that limit the number of nodes/edges in index graphs or are not capable of operating on very large graphs.  ... 
doi:10.1109/icdcsw.2010.42 dblp:conf/icdcsw/KardesG10 fatcat:kek7kwqbazcijhobfk5elefjmi

Resisting structural re-identification in anonymized social networks

Michael Hay, Gerome Miklau, David Jensen, Don Towsley, Chao Li
2010 The VLDB journal  
The approach guarantees anonymity for network entities while preserving the ability to estimate a wide variety of network measures with relatively little bias.  ...  In this paper, we quantify the privacy risks associated with three classes of attacks on the privacy of individuals in networks, based on the knowledge used by the adversary.  ...  We thank the anonymous reviewers for their insightful comments.  ... 
doi:10.1007/s00778-010-0210-x fatcat:jrxjhpd765eerdyauwnvzbyqyi

Anonymizing Social Networks [chapter]

2010 Chapman & Hall/CRC Data Mining and Knowledge Discovery Series  
In this paper, we present a framework for assessing the privacy risk of sharing anonymized network data.  ...  We propose a novel anonymization technique based on perturbing the network and demonstrate empirically that it leads to substantial reduction of the privacy threat.  ...  The complexity of computing H * is linear in the number of edges in the graph, and is therefore efficient even for large datasets.  ... 
doi:10.1201/9781420091502-c15 fatcat:52cze3ar5rfh7iyzgy7gkmu6oy

Replaceable Substructures for Efficient Part-Based Modeling

Han Liu, Ulysse Vimont, Michael Wand, Marie-Paule Cani, Stefanie Hahmann, Damien Rohmer, Niloy J. Mitra
2015 Computer graphics forum (Print)  
The combinatorial complexity of this problem limits existing methods in geometric and/or topological variations of the synthesized models.  ...  We demonstrate the algorithm on a range of test examples producing plausible shape variations, both from a geometric and from a topological viewpoint.  ...  Acknowledgements We are grateful to the anonymous reviewers for their comments, suggestions, and additional references.  ... 
doi:10.1111/cgf.12579 fatcat:bl3jjj3ksjf25pouyyzou3slvq

HiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection [chapter]

Si Zhang, Dawei Zhou, Mehmet Yigit Yildirim, Scott Alcorn, Jingrui He, Hasan Davulcu, Hanghang Tong
2017 Proceedings of the 2017 SIAM International Conference on Data Mining  
Most of the existing methods aim to find a single subgraph with a high density. However, dense subgraphs at different granularities could reveal more intriguing patterns in the underlying graph.  ...  The key idea of our method (HiDDen) is to envision the density of subgraphs as a relative measure to its background (i.e., the subgraph at the coarse granularity).  ...  After we obtain the indicator vector x 1 of the 1 st hierarchy, we solve x 2 under the constraint 0 ≤ x 2 ≤ x 1 by ignoring the constraints of other variables.  ... 
doi:10.1137/1.9781611974973.64 dblp:conf/sdm/ZhangZYAHDT17 fatcat:nm4mkvzmojdqbbzlgwrrrznop4

Sexually Transmitted Infections [chapter]

2011 Nelson Essentials of Pediatrics  
. • properties of neighbors (e.g. mostly friends with republicans) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  to the number of all possible subgraphs of size k. • The pattern subgraph can be efficiently found in the released network, and can be linked to as many as O(log 2 (n)) target nodes. • An un-anonymized  ... 
doi:10.1016/b978-1-4377-0643-7.00116-9 fatcat:jd7d3bkdpjcr3ovfm243sstuua

Sexually Transmitted Infections [chapter]

2013 Encyclopedia of Behavioral Medicine  
. • properties of neighbors (e.g. mostly friends with republicans) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  to the number of all possible subgraphs of size k. • The pattern subgraph can be efficiently found in the released network, and can be linked to as many as O(log 2 (n)) target nodes. • An un-anonymized  ... 
doi:10.1007/978-1-4419-1005-9_101591 fatcat:rstywfexx5hlnjtuu2ytwmihcm

Sexually transmitted infections

Eimear Kieran, Daniel P. Hay
2006 Current Obstetrics and Gynaecology  
. • properties of neighbors (e.g. mostly friends with republicans) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  to the number of all possible subgraphs of size k. • The pattern subgraph can be efficiently found in the released network, and can be linked to as many as O(log 2 (n)) target nodes. • An un-anonymized  ... 
doi:10.1016/j.curobgyn.2006.05.005 fatcat:ths2lmhjpfgavabr27746u5vxa

Sexually transmitted infections

2008 Prescriber  
. • properties of neighbors (e.g. mostly friends with republicans) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  to the number of all possible subgraphs of size k. • The pattern subgraph can be efficiently found in the released network, and can be linked to as many as O(log 2 (n)) target nodes. • An un-anonymized  ... 
doi:10.1002/psb.298 fatcat:yg2f6itbm5c7dda5vqpjkm3ane

Sexually transmitted infections [chapter]

Mike Sharland
2016 OSH Manual of Childhood Infections  
. • properties of neighbors (e.g. mostly friends with republicans) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  to the number of all possible subgraphs of size k. • The pattern subgraph can be efficiently found in the released network, and can be linked to as many as O(log 2 (n)) target nodes. • An un-anonymized  ... 
doi:10.1093/med/9780198729228.003.0032 fatcat:2pc63gmd2rbb3cjcdusigsfg6u

Sexually Transmitted Infections

2004 Adolescent Medicine  
. • properties of neighbors (e.g. mostly friends with republicans) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  to the number of all possible subgraphs of size k. • The pattern subgraph can be efficiently found in the released network, and can be linked to as many as O(log 2 (n)) target nodes. • An un-anonymized  ... 
doi:10.1016/j.admecli.2004.03.002 fatcat:qjc7mmzamfa6vaa2jrqfj4lfyy

Sexually transmitted infections

2005 Independent Nurse  
. • properties of neighbors (e.g. mostly friends with republicans) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  to the number of all possible subgraphs of size k. • The pattern subgraph can be efficiently found in the released network, and can be linked to as many as O(log 2 (n)) target nodes. • An un-anonymized  ... 
doi:10.12968/indn.2005.1.11.74187 fatcat:tb55y3j6dbcrtox5q36n3ndsqq

Sexually Transmitted Infections

2014 AIDS Research and Human Retroviruses  
. • properties of neighbors (e.g. mostly friends with republicans) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  to the number of all possible subgraphs of size k. • The pattern subgraph can be efficiently found in the released network, and can be linked to as many as O(log 2 (n)) target nodes. • An un-anonymized  ... 
doi:10.1089/aid.2014.5635.abstract fatcat:2wgzlddi25d27likgji7vov4ga

5. Sexually Transmitted Infections

2009 Medical and Surgical Dermatology  
. • properties of neighbors (e.g. mostly friends with republicans) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  to the number of all possible subgraphs of size k. • The pattern subgraph can be efficiently found in the released network, and can be linked to as many as O(log 2 (n)) target nodes. • An un-anonymized  ... 
doi:10.1007/s00533-009-0151-9 fatcat:qv3s5yggvjaghk2p4lhf26kjbq
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