From "I have nothing to hide" to "It looks like stalking": Measuring Americans' Level of Comfort with Individual Mobility Features Extracted from Location Data
Naman Awasthi, Saad Mohammad Abrar, Daniel Smolyak, Vanessa Frias-Martinez
TL;DR
This study interrogates Americans' comfort with location data features derived from data brokers, using a factorial vignette survey with 1,405 participants to compare trajectory-based features against POI visits and to assess obfuscation-based privacy protections. It employs a mixed-effects ordinal regression framework and predictive modeling to quantify how actors, purposes, and demographic factors shape privacy perceptions, finding that trajectory data generally reduces comfort while obfuscated representations increase it, sometimes to levels similar to or higher than POIs. The analysis reveals that privacy attitudes and demographic characteristics (notably race/ethnicity and education) modulate these perceptions, and that incorporating attitude data improves predictive accuracy to a moderate degree (F1 around 0.60). The work yields policy-relevant guidance for feature-level privacy controls, obfuscation strategies, and careful consideration of who accesses location data for what purposes, with implications for FTC-like rulemaking and data-broker practices.
Abstract
Location data collection has become widespread with smart phones becoming ubiquitous. Smart phone apps often collect precise location data from users by offering \textit{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 \textbf{0.6}.