Privacy Disclosure Adaptation for Trading between Personal Attributes and Incentives

Abstract

Products and services nowadays need personal information from consumers in order to personalize their goods to best fit consumers. At the present, the online environment is the biggest source of consumers' personal information. However, online privacy has become the major concern of consumers. A personal information trading platform has been proposed as a medium for collecting consumers' personal information in exchange for monetary incentive. This study proposes a new approach to requesting personal attributes which can adapt with consumers' personal information disclosure behavior and aims to increase the disclosure of personal information without increasing of monetary incentive. To develop this new adaption method, we developed the valuation of a personal information method without using currency. The probability and graph mining techniques were used to valuating personal attributes. Then, we displayed the relationships of personal attributes disclosure in the hierarchy and proposed a method for valuating personal information disclosure. The valuation method was used in the evaluations, which were compared with the disclosure of personal information results from the consumers. After the evaluation was completed, the result showed that the new approach can significantly increase the disclosure of consumers' personal information.