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Privacy-Preserving Abuse Detection in Future Decentralised Online Social Networks

Álvaro García-Recuero 1, 2, 3, * Jeffrey Burdges 1 Christian Grothoff 1
* Corresponding author
1 TAMIS - Threat Analysis and Mitigation for Information Security
IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL, Inria Rennes – Bretagne Atlantique
2 DECENTRALISE - DECENTRALISE
Inria Rennes – Bretagne Atlantique
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.
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https://hal.inria.fr/hal-01355951
Contributor : Álvaro García-Recuero <>
Submitted on : Thursday, September 22, 2016 - 9:08:16 PM
Last modification on : Friday, July 10, 2020 - 4:21:33 PM

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Álvaro García-Recuero, Jeffrey Burdges, Christian Grothoff. Privacy-Preserving Abuse Detection in Future Decentralised Online Social Networks. 11th International ESORICS Workshop in Data Privacy Management, DPM 2016, Sep 2016, Heraklion, Crete, Greece. pp.78-93, ⟨10.1007/978-3-319-47072-6_6⟩. ⟨hal-01355951v3⟩

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