"Amid Volumes of Mobile Location Data, New Framework Reduces Consumers' Privacy Risk, Preserves Advertisers' Utility"
The use of mobile technologies to collect and analyze location information on individuals has generated large amounts of consumer location data, further supporting a complex multibillion-dollar system in which consumers can exchange personal data for economic benefits. However, consumers continue to face privacy risks. In a recent study, Machine Learning (ML) was used to develop and test a framework that quantifies personalized privacy risks, performs personalized data obfuscation, and accommodates various risks, utilities, and acceptable levels of risk-utility tradeoff. The framework outperformed previous models, significantly reducing privacy risks for consumers while preserving advertisers' utility. Researchers from Carnegie Mellon University (CMU), the University of Virginia, and New York University conducted the study. This article continues to discuss the new framework that balances privacy risks and data utilities.