Privacy-Preserving Abuse Detection in Future Decentralised Online Social Networks

Álvaro García-Recuero 1, 2, 3, * Jeffrey Burdges 1 Christian Grothoff 1
* Auteur correspondant
1 TAMIS - Threat Analysis and Mitigation for Information Security
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
2 DECENTRALISE - DECENTRALISE
Inria Rennes – Bretagne Atlantique
3 UR1 - Université de Rennes 1
Abstract : Future online social networks need to not only protect sensitive data of their users, but also protect them from abusive behavior coming from malicious participants in the network. We investigate the use of supervised learning techniques to detect abusive behavior and describe privacy-preserving protocols to compute the feature set required by abuse classification algorithms in a secure and privacy-preserving way. While our method is not yet fully resilient against a strong adaptive adversary, our evaluation suggests that it will be useful to detect abusive behavior with a minimal impact on privacy.
Keywords : Online Social Networks Private set intersection cardinality Abuse Detection
Type de document :
Communication dans un congrès
Giovanni Livraga; Vicenç Torra; Alessandro Aldini; Fabio Martinelli; Neeraj Suri. 11th International ESORICS Workshop in Data Privacy Management, DPM 2016, Sep 2016, Heraklion, Crete, Greece. Springer, 9963, pp.78-93, Springer Lecture Notes in Computer Science (LNCS) series. 〈http://dpm2016.di.unimi.it/〉. 〈10.1007/978-3-319-47072-6_6〉
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Contributeur : Álvaro García-Recuero <>
Soumis le : jeudi 22 septembre 2016 - 21:08:16
Dernière modification le : mercredi 16 mai 2018 - 11:24:11

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Álvaro García-Recuero, Jeffrey Burdges, Christian Grothoff. Privacy-Preserving Abuse Detection in Future Decentralised Online Social Networks. Giovanni Livraga; Vicenç Torra; Alessandro Aldini; Fabio Martinelli; Neeraj Suri. 11th International ESORICS Workshop in Data Privacy Management, DPM 2016, Sep 2016, Heraklion, Crete, Greece. Springer, 9963, pp.78-93, Springer Lecture Notes in Computer Science (LNCS) series. 〈http://dpm2016.di.unimi.it/〉. 〈10.1007/978-3-319-47072-6_6〉. 〈hal-01355951v3〉

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