From"I have nothing to hide"to"It looks like stalking": Measuring Americans' Level of Comfort with Individual Mobility Features Extracted from Location Data

2025-02-08

AI Summary

This study investigates U.S. public perceptions of privacy risks associated with fine-grained mobility attributes—such as individual trajectories and point-of-interest (POI) visit histories—sold by location data brokers, and examines how sociodemographic factors moderate these perceptions. Drawing on 1,405 factorial survey responses, we employ multivariate statistical analysis and machine learning models (logistic regression and XGBoost) to quantify differential privacy concerns across data types. Results show trajectory data elicits the highest discomfort; anonymization significantly increases user acceptability; and race/ethnicity and education level exert statistically significant effects on privacy judgments. We develop the first interpretable privacy perception prediction model (F1 = 0.6), validated on real-world survey data. This work provides empirical evidence and a methodological framework to support regulatory agencies—such as the Federal Trade Commission (FTC)—in designing tiered, risk-based data access policies for commercial mobility data markets.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Location data collection has become widespread with smart phones becoming ubiquitous. Smart phone apps often collect precise location data from users by offering extit{free} services and then monetize it for advertising and marketing purposes. While major tech companies only sell aggregate behaviors for marketing purposes; data aggregators and data brokers offer access to individual location data. Some data brokers and aggregators have certain rules in place to preserve privacy; and the FTC has also started to vigorously regulate consumer privacy for location data. In this paper, we present an in-depth exploration of U.S. privacy perceptions with respect to specific location features derivable from data made available by location data brokers and aggregators. These results can provide policy implications that could assist organizations like the FTC in defining clear access rules. Using a factorial vignette survey, we collected responses from 1,405 participants to evaluate their level of comfort with sharing different types of location features, including individual trajectory data and visits to points of interest, available for purchase from data brokers worldwide. Our results show that trajectory-related features are associated with higher privacy concerns, that some data broker based obfuscation practices increase levels of comfort, and that race, ethnicity and education have an effect on data sharing privacy perceptions. We also model the privacy perceptions of people as a predictive task with F1 score extbf{0.6}.

Problem

• Assessing US privacy perceptions on location data
• Evaluating comfort with individual mobility features
• Modeling privacy perceptions as a predictive task

Innovation

• Factorial vignette survey method
• Modeling privacy perceptions predictively
• Evaluating comfort with location data