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.

Too close to lie to you: investigating availability management on multiple communication tools across different social relationships

“…Future studies can leverage log or metadata in order to better understand user behaviors on mobile technologies (e.g. Ghosh and Singh, 2020). Third, our survey examined users' practice of availability management with various social ties as a whole instead of with specific contacts.…”

Guest editorial

“…In this section, we briefly summarise the work presented in these articles. Ghosh and Singh (2018) conducted an experiment to study of phone logged data to predict users' privacy attitudes. As they point out, mobile phones have become omnipresent and the metadata created by the phones reveals a lot about users' habits and preferences.…”

“…Differing from algorithm-centric studies, the main focus of these studies is on how people engage with lifelogging tools. Ghosh and Singh (2018) present a study on users' privacy perception and compare it to their use of their mobile phones. Pingo and Narayan (2019) focus on peoples' use of and interaction with activity trackers.…”

Consumer privacy in smartphones: a systematic literature review

“…The adoption of smartphones has witnessed unprecedented growth globally in the past decade. The growth in security and privacy infringements have impacted the smartphone adoption (Liu et al , 2016; Bonné et al , 2017; Ghosh and Singh, 2018; Albashrawi and Motiwalla, 2019; Banerjee, 2019). The right to privacy has been accepted globally as one of the fundamental rights of every individual.…”

“…The literature related to consumer privacy in smartphones has been covered by researchers from various perspectives (Lian et al , 2013; Cheung, 2014; Arunkumar et al , 2015; Ghosh and Singh, 2018; Wottrich et al , 2018). Although much research on consumer privacy has been carried out and published, the literature on the subject remains largely fragmented.…”