Title: When Simpler Data Does Not Imply Less Information: A Study of User Profiling Scenarios with Constrained View of Mobile HTTP(S) Traffic,
Abstract: The exponential growth in smartphone adoption is contributing to the
availability of vast amounts of human behavioral data. This data enables the
development of increasingly accurate data-driven user models that facilitate
the delivery of personalized services which are often free in exchange for the
use of its customers' data. Although such usage conventions have raised many
privacy concerns, the increasing value of personal data is motivating diverse
entities to aggressively collect and exploit the data. In this paper, we unfold
profiling scenarios around mobile HTTP(S) traffic, focusing on those that have
limited but meaningful segments of the data. The capability of the scenarios to
profile personal information is examined with real user data, collected
in-the-wild from 61 mobile phone users for a minimum of 30 days. Our study
attempts to model heterogeneous user traits and interests, including
personality, boredom proneness, demographics, and shopping interests. Based on
our modeling results, we discuss various implications to personalization,
privacy, and personal data rights.