Predicting User Traits From a Snapshot of Apps Installed on a Smartphone

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Seneviratne, Suranga; Seneviratne, Aruna; Mohapatra, Prasant; Mahanti, Anirban


2014-04-02


Journal Article


ACM SIGMOBILE Mobile Computing and Communications Review


18


Issue 2


1-8


Third party apps are an integral component of the smartphone ecosystem. In this paper, we investigate how user traits can be inferred by observing only a single snapshot of installed apps. Using supervised learning methods and minimal external information we show that user traits such as religion, relationship status, spoken languages, countries of interest, and whether or not the user is a parent of small children, can be easily predicted. Using data collected from over 200 smartphone users, specifically the list of installed apps and the corresponding ground truth traits of the users, we show that for most traits we can achieve over 90% precision. Our inference method can be used to provide services such as personalized content delivery or recommender systems for users. We also highlight privacy loss that can occur from unrestricted access to the app lists in popular smartphone operating systems.


ACM New York, NY, USA


Data61; NICTA; Online Privacy; Smartphones; Smartphone Apps; Semantic Analysis


https://doi.org/10.1145/2636242.2636244


English


nicta:7843


Seneviratne, Suranga; Seneviratne, Aruna; Mohapatra, Prasant; Mahanti, Anirban. Predicting User Traits From a Snapshot of Apps Installed on a Smartphone. ACM SIGMOBILE Mobile Computing and Communications Review. 2014-04-02; 18(Issue 2):1-8. <a href="https://doi.org/10.1145/2636242.2636244" target="_blank">https://doi.org/10.1145/2636242.2636244</a>



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