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.

大量服务的服务依赖于从移动设备收集并与集中实体共享的数据和测量结果。用户享受这些服务（例如基于位置的服务，建议，无线连接等），但以共享其移动设备的数据为代价，从而增加了其隐私风险。例如，用户可能希望接收基于位置的服务，但他们可能会担心将其确切位置发送到服务器或第三方。最近，人们对保留用户隐私的认识和关注，同时仍赋予大数据启用的服务。尽管隐私已成为一个重要的社会问题，但该领域的技术解决方案仍在落后，并且在移动生态系统中的隐私理论与实践之间存在差距。该项目将开发用于控制移动数据的隐私 - 实用性权衡的原则性技术。更广泛的影响活动包括与主要参与者在无线网络和移动数据群体中的合作，培训学生并扩大了计算的参与。在该项目中，我们专门关注移动设备报告的数据，包括有关无线网络的信息，以及个人/用户数据。我们的目标是开发保护隐私的技术来混淆报告的数据，同时仍然为提供服务的质量提供保证。我们将既开发理论框架（隐私性公式，混淆技术的优化和启发式算法），又要开发其在无线频谱共享，蜂窝信号图和个人信息跟踪中的应用。该项目将以原则上透明的方式生产模型，算法，数据集，移动系统实施和编程界面，这些奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。

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

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

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