Investigating Interdependent Privacy Issues in Social App Adoption Scenarios: Theoretical Results and Behavioral Evidence
Open Access
- Author:
- Pu, Yu
- Graduate Program:
- Information Sciences and Technology
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- April 28, 2015
- Committee Members:
- Jens Grossklags, Thesis Advisor/Co-Advisor
- Keywords:
- Social Apps
Interdependent Privacy
Other-Regarding Preferences
App Data Collection Context
Economic Model
Conjoint Analysis - Abstract:
- The popularity of third-party apps on social network sites and mobile networks increasingly highlights the problem of the interdependency of privacy. It is caused by users installing apps that often collect and potentially misuse the personal information of users’ friends who are typically not involved in the decision-making process. We conduct two studies in the area of interdependent privacy to address the existing literature gap on this problem area and to work towards practical solution approaches. Motivated by the theory of other-regarding preferences, our research investigates to which degree users take their friends’ privacy into consideration when they make app adoption decisions. In a first theoretical study, we provide an economic model and simulation results to investigate the adoption of social apps in a network where privacy consequences are interdependent. We present results from two simulations utilizing an underlying scale-free network topology to investigate users’ app adoption behaviors in both an early adoption phase and later adoption periods. The first simulation predictably shows that in the early adoption period, app adoption rates will increase when (1) the interdependent privacy harm caused by an app is lower, (2) installation cost decreases, or (3) network size increases. Surprisingly, we find from the second simulation that app rankings frequently will not accurately reflect the level of interdependent privacy harm when simultaneously considering the adoption results of multiple apps. Given that in the late adoption phase, users make their installation decisions mainly based on app rankings, the simulation results demonstrate that even rational actors who consider their peers’ well-being might adopt apps with significant interdependent privacy harms. In the second study, we take an empirical approach to complement our theoretical work. Applying a conjoint study approach, we conduct the first study to quantify the monetary value which app users place on their friends’ privacy (i.e., value of interdependent privacy). Further, motivated by principles of contextual integrity, we examine the effect of data collection context on the valuation of interdependent privacy. We introduce two survey treatments: (T1) friends’ information is not relevant to app functionality, and (T2) friends’ information is relevant to app functionality. The results show that the monetary value (measured in US$) which individuals place on friends’ complete profile information is $1.56 in T1, and $0.98 in T2. In addition, we find individuals in T1 and T2 valuate their own complete profile information at $2.31 and $2.04, respectively. These valuations are significantly higher than the dollar values they place on friends’ complete profile information. We further measure the impact of comprehensiveness of data collection: an app may collect no information about users’ friends, basic information, or full profile information. Data collection context does not significantly affect how users value their friends’ basic information. However, regarding friends’ sensitive information (i.e., their complete profile), users in T1 valuate such information significantly higher than their counterparts in T2. Both of these two studies contribute to the technology policy discussion on privacy in social apps by calling for meaningful market signals, e.g., designs that can better reflect the level of apps’ interdependent privacy harm, and mechanisms that inform users of apps’ data collection contexts. We believe such signals will help app users to make better informed decisions, and to more accurately address their own and their friends’ privacy preferences.