Online information privacy: Agent-mediated payoff

With the rapid development of applications in open distributed environments such as eCommerce, privacy of information is becoming a critical issue. Information about the preferences, activities, and demographic attributes of people using online shopping is very valuable to online businesses. Beyond the general anxieties with sharing personal information, people may more specifically have concerns about becoming increasingly identifiable; as increasing amounts of personal data are acquired. Not only that, but also it is widely known that information about consumers is often sold to online marketing and advertising companies, generally without the knowledge of consumers [4, 16]. In this paper, we introduce a model where people can opt out to share personal information for a payoff value that balances out the costs of privacy. Our model is based on agents working on behalf of consumers to maximize their benefit. The analysis of the model and a proof of concept implementation are presented in this paper.