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

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.

蜂窝网络提供随时随地便捷的互联网访问，对于网络提供商、最终用户、内容提供商和监管机构来说非常重要，尽管蜂窝网络提供商可以直接测量其网络，但他们越来越多地外包测量。然而，第三方移动分析公司直接从最终用户移动设备收集测量结果，以实现可扩展、低成本、长期和广域的测量。首先，他们不提供其次，他们不会根据设备的位置协调测量，这可能会导致测量偏差或资源浪费。该项目的新颖之处在于设计了用于蜂窝网络纵向协调测量的创新架构和技术。同时向最终用户提供可证明的隐私，可证明的隐私基于新兴的本地差分隐私（LDP）模型，在该模型下，终端设备在位置信息离开设备之前对其进行干扰，因此除了终端设备之外永远不知道实际位置。 .基于该项目的更广泛意义和重要性在于提高基于移动设备的数据收集的隐私意识、招募代表性不足的学生进行研究以及与行业合作。该项目做出了三个主要贡献，首先，它提出了一种基于放大 LDP 的技术，用于高精度地从终端设备收集蜂窝网络测量结果，同时为个人用户提供可证明的隐私；其次，它提出了一种基于优化的测量调度框架来协调。测量第三，它开发了一个数据驱动的模拟工具包，帮助从业者采用测量框架，并使用它来进一步进行用户研究。验证和改进系统。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory

具有有限内存的数据流中的本地差分私有重击检测

• DOI: 10.1145/3639285
• 发表时间: 2024-03
• 期刊: Proceedings of the ACM on Management of Data
• 作者: Li, Xiaochen;Liu, Weiran;Lou, Jian;Hong, Yuan;Zhang, Lei;Qin, Zhan;Ren, Kui
• 通讯作者: Ren, Kui

DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming

DPI：确保无限数据流的严格差异隐私

• 发表时间: 2024-01
• 期刊: 2024 IEEE Symposium on Security and Privacy (SP
• 作者: Feng, S;Mohammady, M;Wang, H;Li, X;Qin, Z;Hong, Y.
• 通讯作者: Hong, Y.