Towards Trustworthy Social Computing Systems

of service abuse that negatively affects the sustainability of these systems and degrades the
quality of service experienced by their users. The main factor that enables service abuse
is the weak identity infrastructure used by most sites, where identities are easy to create
with no verification by a trusted authority. Attackers are exploiting this infrastructure to
launch Sybil attacks, where they create multiple fake (Sybil) identities to take advantage of
the combined privileges associated with the identities to abuse the system.
In this thesis, we present techniques to mitigate service abuse by designing and building
defense schemes that are robust and practical. We use two broad defense strategies: (1)
Leveraging the social network: We first analyze existing social network-based Sybil detection
schemes and present their practical limitations when applied on real world social networks.
Next, we present an approach called Sybil Tolerance that bounds the impact an attacker
can gain from using multiple identities; (2) Leveraging activity history of identities: We
present two approaches, one that applies anomaly detection on user social behavior to detect
individual misbehaving identities, and a second approach called Stamper that focuses on
detecting a group of Sybil identities. We show that both approaches in this category raise
the bar for defense against adaptive attackers.