Iolaus

Abstract

Online content ratings services allow users to find and share content ranging from news articles (Digg) to videos (YouTube) to businesses (Yelp). Generally, these sites allow users to create accounts, declare friendships, upload and rate content, and locate new content by leveraging the aggregated ratings of others. These services are becoming increasingly popular; Yelp alone has over 33 million reviews. Unfortunately, this popularity is leading to increasing levels of malicious activity, including multiple identity (Sybil) attacks and the buying of ratings from users. In this paper, we present Iolaus, a system that leverages the underlying social network of online content rating systems to defend against such attacks. Iolaus uses two novel techniques: (a) weighing ratings to defend against multiple identity attacks and (b) relative ratings to mitigate the effect of bought ratings. An evaluation of Iolaus using microbenchmarks, synthetic data, and real-world content rating data demonstrates that Iolaus is able to outperform existing approaches and serve as a practical defense against multiple-identity and rating-buying attacks.