# Utility-aware and privacy-preserving mobile query services

## TL;DR

This paper introduces StarCloak, a novel privacy-preserving system for mobile location queries on road networks that balances user privacy, utility, and attack resilience, while ensuring scalability and efficiency.

## Contribution

StarCloak is the first to integrate user-defined privacy, utility constraints, and attack-resilience using star-based cloaking graphs on road networks.

## Key findings

- StarCloak improves query success rate and throughput.
- It reduces anonymization time and network usage.
- StarCloak demonstrates higher attack-resilience compared to existing methods.

## Abstract

Location-based queries enable fundamental services for mobile road network travelers. While the benefits of location-based services (LBS) are numerous, exposure of mobile travelers' location information to untrusted LBS providers may lead to privacy breaches. In this paper, we propose StarCloak, a utility-aware and attack-resilient approach to building a privacy-preserving query system for mobile users traveling on road networks. StarCloak has several desirable properties. First, StarCloak supports user-defined k-user anonymity and l-segment indistinguishability, along with user-specified spatial and temporal utility constraints, for utility-aware and personalized location privacy. Second, unlike conventional solutions which are indifferent to underlying road network structure, StarCloak uses the concept of stars and proposes cloaking graphs for effective location cloaking on road networks. Third, StarCloak achieves strong attack-resilience against replay and query injection-based attacks through randomized star selection and pruning. Finally, to enable scalable query processing with high throughput, StarCloak makes cost-aware star selection decisions by considering query evaluation and network communication costs. We evaluate StarCloak on two real-world road network datasets under various privacy and utility constraints. Results show that StarCloak achieves improved query success rate and throughput, reduced anonymization time and network usage, and higher attack-resilience in comparison to XStar, its most relevant competitor.