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Privacy-Preserving Abuse Detection in Future Decentralised Online Social Networks

TitlePrivacy-Preserving Abuse Detection in Future Decentralised Online Social Networks
Publication TypeConference Paper
Year of Publication2016
AuthorsGarcía-Recuero, Á, Burdges, J, Grothoff, C
Refereed DesignationRefereed
Conference NameData Privacy Management (DPM)
Date Published09/2016
PublisherSpringer
Conference LocationHeraklion, Greece
Keywordsabuse, GNUnet, Privacy preserving, reputation, Social networking
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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