TWC: Small: Towards Robust Crowd Computations

这项研究探索了一种基于群体计算的新方法来保护系统，在这种方法中，操作员对群体（系统的任意用户）的意见进行投票，以提供各种推荐服务。例如Yelp、YouTube、Twitter和TripAdvisor等服务。然而，众所周知，今天的服务遭受多重身份（Sybil）攻击，攻击者创建许多身份来破坏系统（例如，使他们的业务在Yelp上看起来更受欢迎）。以前的方法已经研究了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账户或利用现有的黑市来获取假账户或受损账户。相反，PI正在研究一种方法，将Sybil防御从单个Sybil身份检测转移到直接检测大型群体计算本身的操纵。从本质上讲，PI正在将问题从检测单个身份是否为假转移到检测一组身份是否为假，后者对操作员来说可能要容易得多。如果成功，这种方法可以使天平向操作员倾斜，防止攻击者在各种系统中使用虚假、串通和受损害的用户进行操纵。

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.