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Towards a price for private information of mobile users: the Arcade apps in Google Play Store case

Author

Listed:
  • Alessandro De Carolis

    (Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy)

  • Andrea Vitaletti

    (Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy)

Abstract

In this paper we present a first attempt to provide an economic value to mobile users' private information. We claim that when users grant access to an application's required permissions, they disclose private information and data to third parties and let them possibly make revenues out of it. To put a monetary value to such information, we use the price of non-free applications. We use a linear model trained on the 5,187 non-free Arcade apps in Google Play Store that takes a set of permissions in input and estimates the corresponding price. Under the assumption that users "pay" free applications by providing access to more private information (i.e. permissions) and consequently the more permissions are required the less users pay the application, our research aim at showing that the estimated price provides a good proxy to attribute a quantitative value to private and sensitive information of mobile apps' users.

Suggested Citation

  • Alessandro De Carolis & Andrea Vitaletti, 2015. "Towards a price for private information of mobile users: the Arcade apps in Google Play Store case," DIAG Technical Reports 2015-09, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    • Handle: RePEc:aeg:report:2015-09
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    Keywords

    Privacy ; economic value of private information ; application's required permissions ; machine learning ; Google Play Store dataset;
    All these keywords.

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