Personalized Privacy Preservation in Consumer Mobile Trajectories

Abstract

The use of mobile technologies to collect and analyze consumer location data has created a multi-billion-dollar ecosystem with various stakeholders. However, this ecosystem also presents privacy risks to consumers. To address this issue, data aggregators can implement a privacy preserving framework that balances privacy risks to consumers with data utilities for advertisers. The proposed framework is personalized and flexible, allowing for quantification of personalized privacy risks and data obfuscation to reduce these risks. It can accommodate a variety of risks, utilities, and trade-offs between the two. The framework was validated on one million consumer location trajectories, revealing potential privacy risks in the absence of data obfuscation. Machine learning methods are used to demonstrate the effectiveness of the proposed framework which outperformed ten baselines from the latest literature, significantly reducing each consumer’s privacy risk while preserving advertiser utility. As the use of location big data continues to grow, this research offers a necessary framework to balance privacy risks and data utilities, sustain a secure and self-governing ecosystem, and ensure the protection of consumers’ personal data.