Chad Williams

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Publicly accessible adaptive systems such as collaborative recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may inject biased profiles in an attempt to force a system to “adapt” in a manner advantageous to them. Such attacks may lead to a degradation of user trust in the(More)
Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be open to user input, it is difficult to design a system that cannot be so(More)
Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on user-specified judgments for building profiles. Attackers can easily introduce biased data in an attempt to force the system to “adapt” in a manner(More)
Collaborative filtering recommenders are highly vulnerable to malicious attacks designed to affect predicted ratings. Previous work related to detecting such attacks has focused on detecting profiles. Approaches based on profile classification to a large extent depend on profiles conforming to known attack models. In this paper we examine approaches for(More)
Researchers have shown that collaborative recommender systems, the most common type of web personalization system, are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be(More)
Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Researchers have shown that attackers can manipulate a system's recommendations by injecting biased profiles into it. In this paper, we examine attacks that concentrate on a targeted set of users with similar tastes, biasing the system's responses to(More)
Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on userspecified judgments for building profiles. Attackers who cannot be readily distinguished from ordinary users may introduce biased data in an attempt to(More)
Collaborative recommender systems are known to be highly vulnerable to profile injection attacks, attacks that involve the insertion of biased profiles into the ratings database for the purpose of altering the system’s recommendation behavior. Prior work has shown when profiles are reverse engineered to maximize influence; even a small number of malicious(More)
Fluid resuscitation to maintain adequate tissue perfusion while reducing edema in the severely burned patient remains a challenge. Recent studies suggest that reactive oxygen species generated by thermal injury are involved in edema formation associated with burn. The present study tested the hypothesis that adding a free radical scavenger to the(More)
Any alteration in the balance of bacterial challenge versus the host's ability to resist and repair will result in oral lesions that are similar in appearance. The bacterial cause of gingivitis and periodontitis in humans and in all other animals in which it has been studied is firmly established, and specific species of predominantly gram-negative(More)