When Simpler Data Does Not Imply Less Information: A Study of User Profiling Scenarios with Constrained View of Mobile HTTP(S) Traffic
This work addresses privacy concerns in mobile data collection by showing that simplified data can still enable user profiling, which is incremental in understanding data utility vs. privacy trade-offs.
The study investigated whether limited segments of mobile HTTP(S) traffic can still effectively profile user traits like personality and demographics, finding that even constrained data allows accurate modeling of personal information from real-world data of 61 users over 30 days.
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.