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Conference Papers Year : 2016

Privacy-Preserving Abuse Detection in Future Decentralised Online Social Networks

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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Dates and versions

hal-01355951 , version 1 (24-08-2016)
hal-01355951 , version 2 (10-09-2016)
hal-01355951 , version 3 (22-09-2016)

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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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