text: Understanding the demographics of app users is crucial, for example, for app developers, who wish to target their advertisements more effectively. Our work addresses this need by studying the predictability of user demographics based on the list of a user's apps which is readily available to many app developers. We extend previous work on the problem on three frontiers: (1) We predict new demographics (age, race, and income) and analyze the most informative apps for four demographic attributes included in our analysis. The most predictable attribute is gender (82.3 accuracy), whereas the hardest to predict is income (60.3 accuracy). (2) We compare several dimensionality reduction methods for high-dimensional app data, finding out that an unsupervised method yields superior results compared to aggregating the apps at the app category level, but the best results are obtained simply by the raw list of apps. (3) We look into the effect of the training set size and the number of apps on the predictability and show that both of these factors have a large impact on the prediction accuracy. The predictability increases, or in other words, a user's privacy decreases, the more apps the user has used, but somewhat surprisingly, after 100 apps, the prediction accuracy starts to decrease.
  @cite_14: In this paper, we highlight a potential privacy threat in the current smartphone platforms, which allows any third party to collect a snapshot of installed applications without the user's consent. This can be exploited by third parties to infer various user attributes similar to what is done through tracking. We show that using only installed apps, user's gender, a demographic attribute that is frequently used in targeted advertising, can be instantly predicted with an accuracy around 70 , by training a classifier using established supervised learning techniques.
  @cite_6: 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.
summary: The demographic prediction based on user's apps has been previously studied by @cite_14 who predict the users' gender. In their previous work @cite_6 , they also predict language, country, relationship status, and whether the user is a parent, but instead of predicting these attributes directly, they first predict which apps are associated with the attributes and then check whether a user has apps corresponding to a given demographic attribute. We extend these works by studying new demographics (age, race, and income), showing that increasing the training dataset size drastically improves the prediction accuracy, and comparing various dimensionality reduction methods for the app data.