Summary by Ioana Social Turing Tests: Crowd sourcing Sybil Detection -Gang Wang, Manish Mohanlal, Christo Wilson, Xiao Wang, Miriam J. Metzger, Haitao Zheng, Ben Y. Zhao. The paper introduces a crowd sourced detection system for Sybil accounts. These accounts represent fake identities that are created by malicious users, and used in spam and malware. The motivation of this work is represented by the way in which the existence of such accounts affects the online social networks. In the first part of the paper, the authors investigate whether building such a system is feasible or not. More precisely, they are interested in whether Sybil accounts can be accurately detected by users or not, whether different demographic aspects affect the detection process, if the fatigue plays an important role in the detection and how cost effective would be such a system. After investigating all the above aspects, the authors propose a practical system and validate it. The ground-truth data sets of users profiles employed in the paper are obtained from Renren, Facebook India and Facebook US. In order to establish how different users can detect Sybil account the authors conduct a user study. The categories of users used are: - Experts( represented by computer science professors and graduate students ) - Turks( from crowd sourcing websites ) - Sociology undergraduates ; The main finding is the fact that humans can identify between Sybil accounts and legitimate users, but using the majority opinion of a crowd gives more accurate results. The practical aproach of the authors is a two layered system ( filtering and crowdsourcing layer), and the main aspects considered in designing it are scalability, accuracy and privacy. In the validation part, it is showed that the proposed solution produces less that 1% in false positives and negative, and it is cost efficient. Questions : 1. How is the reliability of the users discussed in the paper? 2. What other factors can influence the detection accuracy ( discussed in the user study )? 3. How would you extend the system in order to provide user privacy ? Summary by Patrick This paper presents a system for crowdsourcing Sybil detections. A Sybil is a fake user account on a social network. Mainly used for sending spam targeted at other users. Mechanical Turks are human hired over the internet to do work that is above what machines can do. I can be transcribing audio, video or writing papers. The authors perform an experiment where they attempt to use mechanical turks to determine fake user accounts (Sybil accounts) by having them rate the users profile. They collect data from Facebook and Renren, classifying sets of accounts and having experts and turks rate them. Their results show that experts (People in the field of computer science) are more adept at determining a Sybil account over turks. They suggest a system for partially-automated detection of Sybil accounts. Summary by Zahid Social Turing Tests: Crowdsourcing Sybil Detection Miriam Metzger, Haitao Zheng and Ben Y. Zhao Summary When number of internet users getting increased than there are lot of hackers introduced to hack the systems or data. In the world of internet there are different tools those spread spams and malware like Sybil¡¯s, these tools create lot of spams and malware into Online Social Networks. To detect such spams and malware authors proposed one system called crowedsourced Sybil detection system for Online Social Networks. They conduct large number of user study to detect the Sybil accounts from Facebook and Renner Networks. During the experiments authors detect various conditions of ¡°experts¡± and ¡°turkers¡±. And they get the results from these conditions and they used all these ways to detect the spams and malwares. They used these result to derive the design of multi-tire crowdsourced Sybil detection system. This shows high scalability performance on both ways, like standalone system or as a complimentary technique to current tools.