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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 define and analyze an anonymization problem in undirected graphs, which is motivated by certain privacy issues in social networks.  ...  As a warm up, we first prove that Anonym-V-Del is NP-hard on graphs with maximum degree three.  ... 
doi:10.1007/978-3-642-45030-3_15 fatcat:fhmh4p6wf5apfg5zufafeq4efa

K-isomorphism

James Cheng, Ada Wai-chee Fu, Jia Liu
2010 Proceedings of the 2010 international conference on Management of data - SIGMOD '10  
Our investigations show that k-isomorphism, or anonymization by forming k pairwise isomorphic subgraphs, is both sufficient and necessary for the protection. The problem is shown to be NP-hard.  ...  One popular type of attacks as studied by pioneer work [2] is the use of embedding subgraphs. We follow this line of work and identify two realistic targets of attacks, namely, NodeInfo and LinkInfo.  ...  In order to find good candidates to be inserted into the k i-graphs, here we propose to consider frequent subgraphs that are large.  ... 
doi:10.1145/1807167.1807218 dblp:conf/sigmod/ChengFL10 fatcat:tru6keft3zfypiabds3cg3fjqa

The Complexity of Degree Anonymization by Vertex Addition [chapter]

Robert Bredereck, Vincent Froese, Sepp Hartung, André Nichterlein, Rolf Niedermeier, Nimrod Talmon
2014 Lecture Notes in Computer Science  
That is, after adding these "dummy vertices", for every vertex degree d in the resulting graph, there shall be at least k vertices with degree d.  ...  Motivated by applications in privacy-preserving data publishing, we study the problem to make an undirected graph k-anonymous by adding few vertices (together with incident edges).  ...  Degree Anonymization (vc) is NP-hard and W[2]-hard with respect to the number t of clones, even if the degree k of anonymity is two and the graph is a tree.  ... 
doi:10.1007/978-3-319-07956-1_5 fatcat:xrf5rjzzn5eopf2d4ajgh7z4ri

On the Approximability of Geometric and Geographic Generalization and the Min-Max Bin Covering Problem [article]

Wenliang Du, David Eppstein, Michael T. Goodrich, George S. Lueker
2009 arXiv   pre-print
a single quasi-identifying attribute that represents a geographic or unordered attribute: Zip-codes: nodes of a planar graph generalized into connected subgraphs GPS coordinates: points in R2 generalized  ...  We show that it is impossible to achieve arbitrarily good polynomial-time approximations for a number of natural variations of the generalization technique, unless P = NP, even when the table has only  ...  They then show that if the number of aggregated rows is n and the number of attributes (table columns) is at least 3n, then generalization for k-anonymization is NP-hard.  ... 
arXiv:0904.3756v3 fatcat:zsrb3a3gtjgmlnaha5uoiumuiq

On the Approximability of Geometric and Geographic Generalization and the Min-Max Bin Covering Problem [chapter]

Wenliang Du, David Eppstein, Michael T. Goodrich, George S. Lueker
2009 Lecture Notes in Computer Science  
As is common in existing work on this k-anonymization problem, the means we investigate to perform this anonymization is to generalize values of quasi-identifying attributes into equivalence classes.  ...  These hard single-attribute instances of generalization problems contrast with the previously known NP-hard instances, which require the number of attributes to be proportional to the number of individual  ...  They then show that if the number of aggregated rows is n and the number of attributes (table columns) is at least 3n, then generalization for k-anonymization is NP-hard.  ... 
doi:10.1007/978-3-642-03367-4_22 fatcat:lgrw2nrhqfcdzn7mhsb6nnfq4y

Sexually Transmitted Infections [chapter]

2011 Nelson Essentials of Pediatrics  
Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  ) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  E') is k-degree anonymous. • Approach: Use dynamic programming to finds minimum change to degree sequence. • Challenge: may not be possible to realize degree sequence through edge additions. • Example:  ... 
doi:10.1016/b978-1-4377-0643-7.00116-9 fatcat:jd7d3bkdpjcr3ovfm243sstuua

Sexually Transmitted Infections [chapter]

2013 Encyclopedia of Behavioral Medicine  
Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  ) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  E') is k-degree anonymous. • Approach: Use dynamic programming to finds minimum change to degree sequence. • Challenge: may not be possible to realize degree sequence through edge additions. • Example:  ... 
doi:10.1007/978-1-4419-1005-9_101591 fatcat:rstywfexx5hlnjtuu2ytwmihcm

Sexually transmitted infections

Eimear Kieran, Daniel P. Hay
2006 Current Obstetrics and Gynaecology  
Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  ) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  E') is k-degree anonymous. • Approach: Use dynamic programming to finds minimum change to degree sequence. • Challenge: may not be possible to realize degree sequence through edge additions. • Example:  ... 
doi:10.1016/j.curobgyn.2006.05.005 fatcat:ths2lmhjpfgavabr27746u5vxa

Sexually transmitted infections

2008 Prescriber  
Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  ) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  E') is k-degree anonymous. • Approach: Use dynamic programming to finds minimum change to degree sequence. • Challenge: may not be possible to realize degree sequence through edge additions. • Example:  ... 
doi:10.1002/psb.298 fatcat:yg2f6itbm5c7dda5vqpjkm3ane

Sexually transmitted infections [chapter]

Mike Sharland
2016 OSH Manual of Childhood Infections  
Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  ) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  E') is k-degree anonymous. • Approach: Use dynamic programming to finds minimum change to degree sequence. • Challenge: may not be possible to realize degree sequence through edge additions. • Example:  ... 
doi:10.1093/med/9780198729228.003.0032 fatcat:2pc63gmd2rbb3cjcdusigsfg6u

Sexually Transmitted Infections

2004 Adolescent Medicine  
Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  ) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  E') is k-degree anonymous. • Approach: Use dynamic programming to finds minimum change to degree sequence. • Challenge: may not be possible to realize degree sequence through edge additions. • Example:  ... 
doi:10.1016/j.admecli.2004.03.002 fatcat:qjc7mmzamfa6vaa2jrqfj4lfyy

Sexually transmitted infections

2005 Independent Nurse  
Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  ) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  E') is k-degree anonymous. • Approach: Use dynamic programming to finds minimum change to degree sequence. • Challenge: may not be possible to realize degree sequence through edge additions. • Example:  ... 
doi:10.12968/indn.2005.1.11.74187 fatcat:tb55y3j6dbcrtox5q36n3ndsqq

Sexually Transmitted Infections

2014 AIDS Research and Human Retroviruses  
Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  ) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  E') is k-degree anonymous. • Approach: Use dynamic programming to finds minimum change to degree sequence. • Challenge: may not be possible to realize degree sequence through edge additions. • Example:  ... 
doi:10.1089/aid.2014.5635.abstract fatcat:2wgzlddi25d27likgji7vov4ga

5. Sexually Transmitted Infections

2009 Medical and Surgical Dermatology  
Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  ) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  E') is k-degree anonymous. • Approach: Use dynamic programming to finds minimum change to degree sequence. • Challenge: may not be possible to realize degree sequence through edge additions. • Example:  ... 
doi:10.1007/s00533-009-0151-9 fatcat:qv3s5yggvjaghk2p4lhf26kjbq

5. Sexually Transmitted Infections

2009 Medical and Surgical Dermatology  
Naive anonymization Naive anonymization 3 Attacker finds matches for pattern in naively anonymized network. 4 Attacker re-identifies targets and discloses structural properties.  ...  ) 9 Attacker creates a distinctive subgraph of nodes and edges. 2 Attacker links subgraph to target nodes in the network.  ...  E') is k-degree anonymous. • Approach: Use dynamic programming to finds minimum change to degree sequence. • Challenge: may not be possible to realize degree sequence through edge additions. • Example:  ... 
doi:10.1007/s00533-009-0096-z fatcat:gqzqtjzrkbdfdnu76dmmue5waq
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