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

IMR：MM-1B：具有可证明隐私的蜂窝网络基于纵向终端设备的性能测量

• 批准号: 2319277
• 负责人: Bing Wang
• 金额: $ 59.99万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319277&HistoricalAwards=false
• 关键词: IMR; MM; 1B; Longitudinal; End; IMR; MM; 1B; Longitudinal; End

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.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蜂窝网络可随时随地提供方便地访问Internet。衡量和改善蜂窝网络的性能对于网络提供商，最终用户，内容提供商和监管机构很重要。尽管蜂窝网络提供商可以直接测量其网络，但他们越来越多地将测量值外包给第三方移动分析公司，这些分析公司直接从最终用户移动设备收集测量值，以进行可扩展，低成本，长期和广阔的区域测量。但是，现有的基于移动设备的测量平台有两个主要限制。首先，他们没有为最终用户提供可证明的隐私保证。其次，它们不会根据其位置协调整个设备的测量，这可能导致偏见的测量或浪费资源。该项目的新颖性在于设计创新的体系结构和技术，用于纵向协调的蜂窝网络测量，同时为最终用户提供可证明的隐私。可证明的隐私基于新兴的当地差异隐私（LDP）模型，在该模型下，End设备在离开设备之前扰动位置信息，因此，实际位置在最终设备之外永远都不知道。基于扰动的位置数据，安排并协调了最终设备的测量值，以实现有效的资源使用情况。该项目在提高基于移动设备的数据收集中的隐私意识，招募了代表性不足的学生以及与行业合作的情况下提高对隐私的认识的更大意义和重要性。该项目做出了三个主要贡献。首先，它提出了一种基于放大的LDP技术，用于从最终设备中收集蜂窝网络测量，同时为单个用户提供可证明的隐私。其次，它提出了一个基于优化的测量计划框架，以协调移动设备的测量值，以节省资源使用情况，同时激励测量结果。第三，它开发了一个数据驱动的模拟工具包，该工具包可以帮助从业者采用测量框架。研究团队进一步开发了一个原型系统，并使用它来进行用户研究以进一步验证和改进该系统。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估来获得的支持。