Pricing via Processing or Combatting Junk Mail
CRYPTO '92 Proceedings of the 12th Annual International Cryptology Conference on Advances in Cryptology
An economic answer to unsolicited communication
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Gatekeeper: Monitoring Auto-Start Extensibility Points (ASEPs) for Spyware Management
LISA '04 Proceedings of the 18th USENIX conference on System administration
A Safety-Oriented Platform for Web Applications
SP '06 Proceedings of the 2006 IEEE Symposium on Security and Privacy
Measurement and analysis of spywave in a university environment
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Localization of credential information to address increasingly inevitable data breaches
Proceedings of the 2008 workshop on New security paradigms
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Despite the many solutions proposed by industry and the research community to address spyware, this problem continues to grow. Many of today's anti-spyware approaches are inspired by techniques used against related security problems, such as worms, DoS attacks, computer viruses, and spam. Although these techniques have been retrofitted to address spyware, they remain ineffective because they rely on the compromised host to detect and remove spyware. Once a host is compromised, attackers often find simple ways to escape spyware detection and removal. This paper presents SpySaver - a novel anti-spyware approach that reduces the incentive to deploy spyware. Our approach does not prevent spyware installations, nor does it recover from them. Instead, SpySaver decreases the value of the information spyware collects by creating counterfeit information. Our goal is to generate enough counterfeit information to devalue the information gathered by spyware to the point that we eliminate the incentive to collect it in the first place. In this paper, we present our approach and an initial design of a tool that produces realistic counterfeit information about the browsing patterns of Web users.