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The exponential growth in smartphone adoption is contributing to theavailability of vast amounts of human behavioral data. This data enables thedevelopment of increasingly accurate data-driven user models that facilitatethe delivery of personalized services which are often free in exchange for theuse of its customers' data. Although such usage conventions have raised manyprivacy concerns, the increasing value of personal data is motivating diverseentities to aggressively collect and exploit the data. In this paper, we unfoldprofiling scenarios around mobile HTTP(S) traffic, focusing on those that havelimited but meaningful segments of the data. The capability of the scenarios toprofile personal information is examined with real user data, collectedin-the-wild from 61 mobile phone users for a minimum of 30 days. Our studyattempts to model heterogeneous user traits and interests, includingpersonality, boredom proneness, demographics, and shopping interests. Based onour modeling results, we discuss various implications to personalization,privacy, and personal data rights.

When Simpler Data Does Not Imply Less Information: A Study of User Profiling Scenarios with Constrained View of Mobile HTTP(S) Traffic