Learn More
Reputation systems are playing critical roles in securing today's distributed computing and communication systems. Similar to other security mechanisms, reputation systems can be under attack. In this paper, we report the discovery of a new attack, named RepTrap(Reputation Trap), against feedback-based reputation systems, such as those used in P2P(More)
Free-riders and fake files are two important problems in P2P file sharing systems. Previous works have always used incentive mechanisms and trust mechanisms to address them respectively. In real systems however, a trust mechanism without incentive would face lack of users' enthusiasm and thus cause sparse relationship of direct trust while an incentive(More)
Social trust and recommendation services are the most popular social rating systems today for service providers to learn about the social opinion or popularity of a product, item, or service, such as a book on Amazon, a seller on eBay, a story on Digg or a movie on Netflix. Such social rating systems are very convenient and offer alternative learning(More)
P2P file sharing systems often use incentive policies to encourage sharing. With the decrease of free riders, the amount of cheating behaviors has increased. Some users rename a common file with a popular name to attract the downloads of other users in order to gain unfair advantages from incentive policies. We call the renamed file a fake file. While(More)
With the popularity of voting systems in cyberspace, there is growing evidence that current voting systems can be manipulated by fake votes. This problem has attracted many researchers working on guarding voting systems in two areas: relieving the effect of dishonest votes by evaluating the trust of voters, and limiting the resources that can be used by(More)
Online product ranking is complicated by conflicting attributes. The authors propose a rainbow ranking system to solve this problem in e-commerce. This approach extends eBay's one-to-one shopping paradigm to a flexible one with many-to-many transactions. The system demonstrates a 50 to 70 percent transaction increase over skyline and eBay ranking methods.(More)
Recently, online rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but challenging problem. Many unfair rating detection approaches have been developed and evaluated against simple attack models. However, the lack of unfair rating data from real human users and realistic attack behavior(More)
Online feedback-based rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important problem and many unfair rating detection approaches have been developed. Currently, these approaches are evaluated against simple attack models, but complicated attacking strategies can be used by attackers in the real(More)
This paper presents MAPS — a personalized Multi-Attribute Probabilistic Selection framework — to estimate the probability of an item being a user's best choice and rank the items accordingly. The MAPS framework makes three original contributions in this paper. First, we capture the inter-attribute tradeoff by a visual angle model which maps(More)
User behavior models are important for building realistic simulation environment for research on P2P multimedia file-sharing systems. In this paper, we build a user download behavior model and a user removal behavior model, which can describe important user characteristics that are not captured in the existing models. The proposed download behavior model(More)