This HTML5 document contains 24 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dcthttp://purl.org/dc/terms/
dbohttp://dbpedia.org/ontology/
foafhttp://xmlns.com/foaf/0.1/
n4https://global.dbpedia.org/id/
dbthttp://dbpedia.org/resource/Template:
rdfshttp://www.w3.org/2000/01/rdf-schema#
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
owlhttp://www.w3.org/2002/07/owl#
wikipedia-enhttp://en.wikipedia.org/wiki/
dbchttp://dbpedia.org/resource/Category:
provhttp://www.w3.org/ns/prov#
dbphttp://dbpedia.org/property/
xsdhhttp://www.w3.org/2001/XMLSchema#
wikidatahttp://www.wikidata.org/entity/
dbrhttp://dbpedia.org/resource/

Statements

Subject Item
dbr:Differential_privacy
dbo:wikiPageWikiLink
dbr:Differentially_private_analysis_of_graphs
Subject Item
dbr:Differentially_private_analysis_of_graphs
rdfs:label
Differentially private analysis of graphs
rdfs:comment
Differentially private analysis of graphs studies algorithms for computing accurate graph statistics while preserving differential privacy. Such algorithms are used for data represented in the form of a graph where nodes correspond to individuals and edges correspond to relationships between them. For examples, edges could correspond to friendships, sexual relationships, or communication patterns. A party that collected sensitive graph data can process it using a differentially private algorithm and publish the output of the algorithm. The goal of differentially private analysis of graphs is to design algorithms that compute accurate global information about graphs while preserving privacy of individuals whose data is stored in the graph.
dct:subject
dbc:Differential_privacy dbc:Information_privacy
dbo:wikiPageID
60504494
dbo:wikiPageRevisionID
1104988664
dbo:wikiPageWikiLink
dbr:Randomness dbr:Real_number dbr:Randomized_algorithm dbc:Information_privacy dbr:Differential_privacy dbr:Sofya_Raskhodnikova dbc:Differential_privacy dbr:Algorithm
owl:sameAs
n4:ABzSp wikidata:Q65057618
dbp:wikiPageUsesTemplate
dbt:Reflist
dbo:abstract
Differentially private analysis of graphs studies algorithms for computing accurate graph statistics while preserving differential privacy. Such algorithms are used for data represented in the form of a graph where nodes correspond to individuals and edges correspond to relationships between them. For examples, edges could correspond to friendships, sexual relationships, or communication patterns. A party that collected sensitive graph data can process it using a differentially private algorithm and publish the output of the algorithm. The goal of differentially private analysis of graphs is to design algorithms that compute accurate global information about graphs while preserving privacy of individuals whose data is stored in the graph.
prov:wasDerivedFrom
wikipedia-en:Differentially_private_analysis_of_graphs?oldid=1104988664&ns=0
dbo:wikiPageLength
6159
foaf:isPrimaryTopicOf
wikipedia-en:Differentially_private_analysis_of_graphs
Subject Item
dbr:Sofya_Raskhodnikova
dbo:wikiPageWikiLink
dbr:Differentially_private_analysis_of_graphs
Subject Item
wikipedia-en:Differentially_private_analysis_of_graphs
foaf:primaryTopic
dbr:Differentially_private_analysis_of_graphs