Evaluating the Privacy Valuation of Personal Data on Smartphones

Abstract

Smartphones hold a great variety of personal data during usage, which at the same time poses privacy risks. In this paper, we used the selling price to reflect users' privacy valuation of their personal data on smartphones. In a 7-day auction, they sold their data as commodities and earn money. We first designed a total of 49 commodities with 8 attributes, covering 14 common types of personal data on smartphones. Then, through a large-scale reverse second price auction (N=181), we examined students' valuation of 15 representative commodities. The average bid-price was 62.8 CNY (8.68 USD) and a regression model with 14 independent variables found the most influential factors for bid-price to be privacy risk, ethnic and gender. When validating our results on non-students (N=34), we found that despite they gave significantly higher prices (M=109.8 CNY, 15.17 USD), "privacy risk" was still one of the most influential factors among the 17 independent variables in the regression model. We recommended that stakeholders should provide 8 attributes of data when selling or managing it.