TWC: Small: Towards Robust Crowd Computations

这项研究探索了一种保护基于人群计算的系统安全的新方法，其中操作员会轮询人群（系统的任意用户）的意见，以提供各种推荐服务。 示例包括 Yelp、YouTube、Twitter 和 TripAdvisor 等服务。 然而，众所周知，当今的服务会遭受多重身份 (Sybil) 攻击，攻击者会创建许多身份来颠覆系统（例如，让他们的业务在 Yelp 上显得更受欢迎）。 以前的方法已经研究过检测单个身份是否可能是假的，但这些技术在实践中存在许多缺点，因为攻击者通常能够创建许多假帐户或利用现有的黑市来获取假帐户或受损帐户。 相反，PI 正在研究一种方法，将 Sybil 防御从单个 Sybil 身份检测转向直接检测大规模人群计算本身的操纵。 从本质上讲，PI 正在将问题从检测单个身份是否是假的转移到检测一组身份是否是假的，后者对于操作员来说可能要容易得多。 如果成功，该方法可以使天平向有利于操作员的方向倾斜，防止攻击者在各种系统中使用虚假、共谋和受损的用户进行操纵。

This research explores a new approach to securing systems that are based on crowd computations, where the operator polls the opinions of crowds--arbitrary users of the system--to provide a variety of recommendation services. Examples include services like Yelp, YouTube, Twitter, and TripAdvisor. However, today's services are known to suffer from multiple identity (Sybil) attacks, where an attacker creates many identities to subvert the system (e.g., make their business appear to be more popular on Yelp). Previous approaches have investigated detecting whether a single identity is likely to be fake, but these techniques suffer from a number of drawbacks in practice, as attackers are often able to create many fake accounts or leverage existing black-markets for fake or compromised accounts. Instead, the PI is investigating an approach that shifts Sybil defense away from individual Sybil identity detection and towards directly detecting manipulation of large crowd computations themselves. In essence, the PI is shifting the problem from detecting whether a single identity is fake to detecting whether a set of identities are fake, the latter of which is likely to be significantly easier for the operator. If successful, the approach could tip the scales back in favor of the operator, preventing manipulation from attackers using fake, colluding, and compromised users in a variety of systems.