Anonymizing Social Graphs via Uncertainty Semantics
Identifieur interne : 000585 ( Main/Exploration ); précédent : 000584; suivant : 000586Anonymizing Social Graphs via Uncertainty Semantics
Auteurs : Hiep H. Nguyen [France] ; Abdessamad Imine [France] ; Michaël Rusinowitch [France]Source :
English descriptors
Abstract
Rather than anonymizing social graphs by generalizing them to super nodes/edges or adding/removing nodes and edges to satisfy given privacy parameters, recent methods exploit the semantics of uncertain graphs to achieve privacy protection of participating entities and their relationships. These techniques anonymize a deterministic graph by converting it into an uncertain form. In this paper, we propose a general obfuscation model based on uncertain adjacency matrices that keep expected node degrees equal to those in the unanonymized graph. We analyze two recently proposed schemes and their fitting into the model. We also point out disadvantages in each method and present several elegant techniques to fill the gap between them. Finally, to support fair comparisons, we develop a new tradeoff quantifying framework by leveraging the concept of incorrectness. Experiments on large social graphs demonstrate the effectiveness of our schemes.
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Affiliations:
- France
- Franche-Comté, Grand Est, Lorraine (région)
- Belfort, Besançon, Metz, Nancy
- Université de Bourgogne Franche-Comté, Université de Franche-Comté, Université de Lorraine, Université de technologie de Belfort-Montbéliard
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Le document en format XML
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These techniques anonymize a deterministic graph by converting it into an uncertain form. In this paper, we propose a general obfuscation model based on uncertain adjacency matrices that keep expected node degrees equal to those in the unanonymized graph. We analyze two recently proposed schemes and their fitting into the model. We also point out disadvantages in each method and present several elegant techniques to fill the gap between them. Finally, to support fair comparisons, we develop a new tradeoff quantifying framework by leveraging the concept of incorrectness. Experiments on large social graphs demonstrate the effectiveness of our schemes.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Franche-Comté</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Belfort</li><li>Besançon</li><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Bourgogne Franche-Comté</li><li>Université de Franche-Comté</li><li>Université de Lorraine</li><li>Université de technologie de Belfort-Montbéliard</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Nguyen, Hiep H" sort="Nguyen, Hiep H" uniqKey="Nguyen H" first="Hiep H." last="Nguyen">Hiep H. Nguyen</name></region><name sortKey="Imine, Abdessamad" sort="Imine, Abdessamad" uniqKey="Imine A" first="Abdessamad" last="Imine">Abdessamad Imine</name><name sortKey="Rusinowitch, Michael" sort="Rusinowitch, Michael" uniqKey="Rusinowitch M" first="Michaël" last="Rusinowitch">Michaël Rusinowitch</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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