Preliminary Exam Talks
Aiding the Detection of Fake Accounts in Large Scale Social Online Services
qiangcao at cs.duke.edu
||Tuesday, July 3, 2012
||8:00am - 10:00am
||D344 LSRC, Duke
||Jeffrey Chase, Bruce Maggs, Michael Sirivianos
Online Social Networks (OSNs) play a significant role in our daily life. Users increasingly rely on the trustworthiness of the information exposed on OSNs, and OSN providers base their business models on the marketability of this information. However, OSNs suffer from abuse in the form of the creation of fake accounts, which do not correspond to real humans. Fake accounts can introduce spam, manipulate online rating, or exploit knowledge extracted from the network. OSN operators currently expend significant resources to detect, manually verify, and shut down fake accounts. Tuenti, the largest OSN in Spain, dedicates 14 full-time employees in that task alone, incurring a significant monetary cost. Such a task has yet to be successfully automated because of the difficulty in reliably capturing the diverse behavior of fake and real OSN profiles.
In this talk, I will present our work that aids the detection of fake accounts in large scale OSNs. We introduce a new tool in the hands of OSN operators, which we call SybilRank. It relies on social graph properties to rank users according to their perceived likelihood of being fake (Sybils). SybilRank is computationally efficient and can scale to social graphs with hundreds of millions of nodes, as demonstrated by our Hadoop prototype. We deployed SybilRank in Tuenti's operation center. We found that ~90% of the 200K accounts that SybilRank designated as most likely to be fake, actually warranted suspension. On the other hand, with Tuenti's current user-report-based approach only ~5% of the inspected accounts are indeed fake.
Advisor(s): Xiaowei Yang