IMR: MM-1B: Longitudinal End-device based Performance Measurement of Cellular Networks with Provable Privacy

Supported by NSF CNS-2319277

Cellular networks provide convenient access to the Internet anytime and anywhere. Measuring and improving the performance of cellular networks is important to network providers, end users, content providers, and regulators. While cellular network providers can directly measure their networks, they increasingly outsource the measurements to third-party mobile analytics companies, which collect measurements directly from end-user mobile devices for scalable, low-cost, long-term, and wide-area measurements. Existing mobile-device based measurement platforms, however, have two major limitations. First, they do not provide provable privacy guarantees to end users. Second, they do not coordinate measurements across the devices based on their locations, which can lead to biased measurements or wasted resources. This project’s novelties are in designing innovative architecture and techniques for longitudinal coordinated measurements of cellular networks, while providing provable privacy to end users. The provable privacy is based on the emerging local differential privacy (LDP) model, under which end devices perturb the location information before it leaves the devices, and hence the actual locations are never known beyond the end devices. Based on perturbed location data, the measurements at end devices are scheduled and coordinated to achieve efficient resource usage. The project's broader significance and importance are in raising awareness in privacy in mobile-device based data collection, recruiting underrepresented students in research, and collaborating with industry.

This project makes three main contributions. First, it proposes an amplified LDP based technique for collecting cellular network measurements from end devices with high accuracy, while providing provable privacy to individual users. Second, it proposes an optimization-based measurement scheduling framework to coordinate the measurements at the mobile devices to conserve resource usage, while incentivizing measurements. Third, it develops a data-driven simulation toolkit that assists practitioners to adopt the measurement framework. The research team further develops a prototype system and uses it to conduct a user study to further validate and improve the system.

People

Dr. Suining He
Dr. Yuan Hong
Dr. Bing Wang (PI)
Yawen Deng
Md Mahbub Hasan
Md Zakir Hussein
Nima Naderloui