IMR:MM-1B: New directions in Privacy-Preserving Telemetry

几乎所有现代设备都连接到互联网。家电、智能手表、手机、汽车、工业工具，甚至体重秤都被连接起来。例如，三星智能电视向三星报告消费者的每一个选择和每一次点击，以及消费者观看任何节目的持续时间。互联网浏览器收集用户浏览数据。互联网服务提供商收集用户IP访问数据。收集的数据洪流通常用于改善用户体验、服务和目标广告。然而，收集有关每个消费者或组织的更具侵入性的数据是以颤抖的隐私侵犯为代价的，并增加了收集的数据可能被用于其他（非预期）目的的风险，例如反情报，政治活动以及身份盗窃和其他犯罪活动。我们如何允许组织在不侵犯个人消费者隐私的情况下收集有关流数据的汇总统计数据？这项研究的目的是探索新的方法来计算聚合统计流数据的隐私保护的方式，扩展系统，如PRIO，PRIO+，和白杨。 该框架是用户或设备以秘密共享的方式将其数据发送到两个服务器，然后彼此通信以计算遥测数据，同时不泄露（彼此或任何其他人）用户的个人数据。例如，Firefox浏览器在名为“Origin Telemetry”的Mozilla项目中采用了这种方法。这项研究的目标是探索更有效的方法来私下计算遥测数据在这种情况下，探索如何推广流算法（没有隐私），由阿隆，马蒂亚斯和Szegedy开创的流算法的隐私。更具体地说，我们可以通过两个接收（秘密共享）流的服务器以隐私保护和有效的方式计算频率矩吗？虽然这看起来像是一个非常专业的问题，但事实上，它是可推广的，正如PI在题为“零一频率定律”的论文中所示。这给我们带来了一个更有趣的问题：如何对所有可以在一次通过和多对数内存中通过流秘密共享数据进行私有计算的函数进行分类。 我们的目标是提供新的工具，用于流媒体大容量数据的隐私保护分析问题，特别是在NSF互联网测量研究呼吁提案的MM-1B部分中。如果成功的话，我们的方法将允许大规模和有效的计算所有聚合统计数据，可以计算在小内存中的隐私保护的方式。该项目还将推进MPC在流数据上的最新性能，以实现互联网大小的流计算。最后，我们的提案呼吁对研究生和本科生进行强有力的培训，包括积极寻求少数民族和女学生进入密码研究。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。

Almost all modern-day devices are connected to the internet. Home appliances, smart watches, phones, cars, industrial tools, and even body weight scales are connected. For example, Samsung Smart TV reports back to Samsung every choice and every click that a consumer makes as well as the duration that a consumer watches any program. Internet browsers collect user browsing data. Internet Service Providers collect user IP access data. The torrent of data collected is typically used to improve user experience, service, and target advertisements. However, collection of ever more intrusive data regarding each individual consumer or organization comes at the price of tremulous invasion of privacy, and increases the risk that the data collected can be harvested for other (unintended) purposes, such as counter-intelligence, political campaigns, as well as identity theft and other criminal activity. How do we allow organizations to collect aggregate statistics regarding streaming data without violating individual consumer privacy?This research aims to explore novel ways to compute aggregate statistics on streaming data in a privacy-preserving way, extending systems such as PRIO, PRIO+, and Poplar. The framework is that users or devices send their data in a secret-shared way to two servers which then communicate with each other to compute telemetry data while not revealing (to each other or anyone else) users’ individual data. This approach was adopted, for example, by the Firefox browser in a Mozilla project titled “Origin Telemetry”. The goals of this research are to explore even more efficient methods to privately compute telemetry data in this setting by exploring how to generalize streaming algorithms (without privacy) that were pioneered by Alon, Matias, and Szegedy to streaming algorithms with privacy. More specifically, can we compute frequency moments in a privacy-preserving and efficient manner by two servers receiving a (secret-shared) stream? While this seems like a very specialized question, it is, in fact, generalizable, as shown by the PI in the paper titled “Zero-One Frequency Laws”. This brings us to the even more interesting question: how to classify all functions that can be privately computed over streaming secret-shared data in one pass and with poly-logarithmic memory. Our goal is to provide new tools for the questions of privacy-preserving analysis of streaming large volume data, specifically called out in section MM-1B of the NSF Internet Measurement Research call for proposals. If successful, our methods will allow large-scale and efficient computation of all aggregate statistics that can be computed in small memory in a privacy-preserving way. This project will also advance the state-of-the-art performance of MPC on streaming data to Internet-size streaming computations. Lastly, our proposal calls for robust training of graduate and undergraduate students, including actively seeking minorities and female students to enter cryptographic research.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.