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

TL;DR

This study investigates how limited segments of mobile HTTP(S) traffic can still reveal significant personal information, highlighting privacy risks in user profiling despite constrained data views.

Contribution

It demonstrates that even restricted mobile traffic data can effectively profile user traits, raising important privacy considerations and informing data handling practices.

Findings

Limited mobile traffic segments can accurately predict user personality and interests

Profiling accuracy remains high despite data constraints

Implications for privacy and data rights are significant and warrant attention

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.