Phones, privacy, and predictions

Abstract

Purpose Mobile phones have become one of the most favored devices to maintain social connections as well as logging digital information about personal lives. The privacy of the metadata being generated in this process has been a topic of intense debate over the last few years, but most of the debate has been focused on stonewalling such data. At the same time, such metadata is already being used to automatically infer a user’s preferences for commercial products, media, or political agencies. The purpose of this paper is to understand the predictive power of phone usage features on individual privacy attitudes. Design/methodology/approach The present study uses a mixed-method approach, involving analysis of mobile phone metadata, self-reported survey on privacy attitudes and semi-structured interviews. This paper analyzes the interconnections between user’s social and behavioral data as obtained via their phone with their self-reported privacy attitudes and interprets them based on the semi-structured interviews. Findings The findings from the study suggest that an analysis of mobile phone metadata reveals vital clues to a person’s privacy attitudes. This study finds that multiple phone signals have significant predictive power on an individual’s privacy attitudes. The results motivate a newer direction of automatically inferring a user’s privacy attitudes by leveraging their phone usage information. Practical implications An ability to automatically infer a user’s privacy attitudes could allow users to utilize their own phone metadata to get automatic recommendations for privacy settings appropriate for them. This study offers information scientists, government agencies and mobile app developers, an understanding of user privacy needs, helping them create apps that take these traits into account. Originality/value The primary value of this paper lies in providing a better understanding of the predictive power of phone usage features on individual privacy attitudes.