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

2013
*
Lecture Notes in Computer Science
*

The goal

doi:10.1007/978-3-642-45030-3_15
fatcat:fhmh4p6wf5apfg5zufafeq4efa
*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. ...##
###
K-isomorphism

2010
*
Proceedings of the 2010 international conference on Management of data - SIGMOD '10
*

Our investigations show that k-isomorphism, or

doi:10.1145/1807167.1807218
dblp:conf/sigmod/ChengFL10
fatcat:tru6keft3zfypiabds3cg3fjqa
*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*. ...##
###
The Complexity of Degree Anonymization by Vertex Addition
[chapter]

2014
*
Lecture Notes in Computer Science
*

That

doi:10.1007/978-3-319-07956-1_5
fatcat:xrf5rjzzn5eopf2d4ajgh7z4ri
*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. ...##
###
On the Approximability of Geometric and Geographic Generalization and the Min-Max Bin Covering Problem
[article]

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

arXiv:0904.3756v3
fatcat:zsrb3a3gtjgmlnaha5uoiumuiq
*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*. ...##
###
On the Approximability of Geometric and Geographic Generalization and the Min-Max Bin Covering Problem
[chapter]

2009
*
Lecture Notes in Computer Science
*

As

doi:10.1007/978-3-642-03367-4_22
fatcat:lgrw2nrhqfcdzn7mhsb6nnfq4y
*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*. ...##
###
Sexually Transmitted Infections
[chapter]

2011
*
Nelson Essentials of Pediatrics
*

Naive

doi:10.1016/b978-1-4377-0643-7.00116-9
fatcat:jd7d3bkdpjcr3ovfm243sstuua
*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: ...##
###
Sexually Transmitted Infections
[chapter]

2013
*
Encyclopedia of Behavioral Medicine
*

Naive

doi:10.1007/978-1-4419-1005-9_101591
fatcat:rstywfexx5hlnjtuu2ytwmihcm
*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: ...##
###
Sexually transmitted infections

2006
*
Current Obstetrics and Gynaecology
*

Naive

doi:10.1016/j.curobgyn.2006.05.005
fatcat:ths2lmhjpfgavabr27746u5vxa
*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: ...##
###
Sexually transmitted infections

2008
*
Prescriber
*

*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: ...

##
###
Sexually transmitted infections
[chapter]

2016
*
OSH Manual of Childhood Infections
*

*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: ...

##
###
Sexually Transmitted Infections

2004
*
Adolescent Medicine
*

*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: ...

##
###
Sexually transmitted infections

2005
*
Independent Nurse
*

*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: ...

##
###
Sexually Transmitted Infections

2014
*
AIDS Research and Human Retroviruses
*

*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: ...

##
###
5. Sexually Transmitted Infections

2009
*
Medical and Surgical Dermatology
*

*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: ...

##
###
5. Sexually Transmitted Infections

2009
*
Medical and Surgical Dermatology
*

*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: ...

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