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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Acknowledgments
We thank the Renewable Freedom Foundation for supporting this research, the volunteers who annotated abuse and the anonymous reviewers. Special thanks to Cristina Onete for pointing us towards PSI protocol literature.
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García-Recuero, Á., Burdges, J., Grothoff, C. (2016). Privacy-Preserving Abuse Detection in Future Decentralised Online Social Networks. In: Livraga, G., Torra, V., Aldini, A., Martinelli, F., Suri, N. (eds) Data Privacy Management and Security Assurance. DPM QASA 2016 2016. Lecture Notes in Computer Science(), vol 9963. Springer, Cham. https://doi.org/10.1007/978-3-319-47072-6_6
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DOI: https://doi.org/10.1007/978-3-319-47072-6_6
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