CNS Core: Medium: Collaborative Research: Privacy-Preserving Mobile Crowdsourced Data

CNS 核心：媒介：协作研究：保护隐私的移动众包数据

• 批准号: 1901488
• 负责人: Konstantinos Psounis
• 金额: $ 65万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901488&HistoricalAwards=false
• 关键词: CNS; Core; Medium; Collaborative; Research

A large and increasing number of services rely on data and measurements collected from mobile devices and shared with a centralized entity. Users enjoy these services (such as location-based services, recommendations, wireless connectivity and others) at the expense of sharing data from their mobile devices, which increases their privacy risk. For example, users may want to receive a location-based service, but they may be concerned about sending their exact location to a server or third parties. Recently, there has been increased awareness and concern about preserving user privacy, while still empowering the services that big data enable. While privacy has become a significant societal concern, the technical solutions in this domain are still lagging behind, and there is a gap between the theory of privacy and practices in the mobile ecosystem. This project will develop principled techniques for controlling the privacy-utility tradeoff for mobile data. Broader impact activities include collaborations with major players in the space of wireless networking and mobile data crowdsourcing, training students and broadening participation in computing.In this project, we focus specifically on data reported from mobile devices, including information about the wireless network, as well as personal/user data. Our goal is to develop privacy-preserving techniques to obfuscate reported data, while still providing guarantees for the quality of the provided service. We will develop both the theoretical framework (privacy-utility formulation, optimization of obfuscation techniques, and heuristic algorithms), as well as its application to wireless spectrum sharing, cellular signal maps, and personal information tracking. The project will produce models, algorithms, datasets, mobile system implementations and programming interfaces, which trade off utility vs. privacy in a principled and transparent way.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.

大量且越来越多的服务依赖于从移动的设备收集并与集中式实体共享的数据和测量。用户享受这些服务（例如基于位置的服务、推荐、无线连接等）的代价是从他们的移动的设备共享数据，这增加了他们的隐私风险。例如，用户可能希望接收基于位置的服务，但他们可能担心将其确切位置发送给服务器或第三方。最近，人们对保护用户隐私的意识和关注有所提高，同时仍然为大数据所支持的服务提供支持。虽然隐私已经成为一个重要的社会问题，但该领域的技术解决方案仍然落后，隐私理论与移动的生态系统中的实践之间存在差距。该项目将开发用于控制移动的数据的隐私-效用权衡的原则性技术。更广泛的影响活动包括与无线网络和移动的数据众包领域的主要参与者合作，培训学生和扩大计算的参与。在这个项目中，我们特别关注从移动的设备报告的数据，包括有关无线网络的信息，以及个人/用户数据。我们的目标是开发隐私保护技术来混淆报告的数据，同时仍然为所提供的服务质量提供保证。我们将开发理论框架（隐私效用公式，混淆技术的优化和启发式算法），以及其在无线频谱共享，蜂窝信号地图和个人信息跟踪中的应用。该项目将产生模型、算法、数据集、移动的系统实现和编程接口，以原则和透明的方式权衡实用性与隐私性。该奖项反映了NSF的法定使命，并通过使用基金会的知识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

Optimizing Primary User Privacy in Spectrum Sharing Systems

• DOI: 10.1109/tnet.2020.2967776
• 发表时间: 2020-02
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Matthew A. Clark;K. Psounis

No Video Left Behind: A Utility-Preserving Obfuscation Approach for YouTube Recommendations

• DOI: 10.48550/arxiv.2210.08136
• 发表时间: 2022
• 期刊: ArXiv
• 作者: Jiang Zhang;Hadi Askari;Konstantinos Psounis;Zubair Shafiq

Privacy-utility trades in crowdsourced signal map obfuscation

众包信号图混淆中的隐私-实用性交易

• DOI: 10.1016/j.comnet.2022.109187
• 发表时间: 2022
• 期刊: Computer Networks
• 影响因子: 5.6
• 作者: Zhang, Jiang;Clark, Lillian;Clark, Matthew;Psounis, Konstantinos;Kairouz, Peter
• 通讯作者: Kairouz, Peter

A Unified Prediction Framework for Signal Maps: Not All Measurements are Created Equal

信号图的统一预测框架：并非所有测量结果都是平等的

• DOI: 10.1109/tmc.2022.3221773
• 发表时间: 2022
• 期刊: IEEE Transactions on Mobile Computing
• 影响因子: 7.9
• 作者: Alimpertis, Emmanouil;Markopoulou, Athina;Butts, Carter T.;Bakopoulou, Evita;Psounis, Konstantinos
• 通讯作者: Psounis, Konstantinos

HARPO: Learning to Subvert Online Behavioral Advertising

• DOI: 10.14722/ndss.2022.23062
• 发表时间: 2021-11
• 期刊: ArXiv
• 作者: Jiang Zhang;K. Psounis;Muhammad Haroon;Zubair Shafiq