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

IMR:MM-1B：隐私保护遥测的新方向

基本信息

批准号：
2220450
负责人：
Rafail Ostrovsky
金额：
$ 60万
依托单位：
University of California-Los Angeles
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220450&HistoricalAwards=false
关键词：
IMR MM 1B New directions

项目摘要

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.

几乎所有现代设备都连接到互联网，例如，三星智能电视向三星报告每一个选择和每一次点击。消费者观看任何节目的时间互联网服务提供商收集用户 IP 访问数据通常用于改善用户体验、服务和目标广告。收集越来越多的侵入性数据关于每个个人消费者或组织的信息的代价是严重侵犯隐私，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、政治运动以及身份盗窃和其他目的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？本研究旨在探索以保护隐私的方式计算流数据汇总统计数据的新方法，扩展 PRIO、PRIO+、和杨树。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，然后相互通信以计算遥测数据，同时不泄露（向彼此或其他任何人）用户的个人数据。例如，通过名为“Origin Telemetry”的 Mozilla 项目中的 Firefox 浏览器，本研究的目标是通过探索如何推广开创性的流算法（无隐私），探索在此设置中私密计算遥测数据的更有效方法。通过阿隆， Matias 和 Szegedy 讨论了具有隐私性的流算法。更具体地说，我们能否通过接收（秘密共享）流的两个服务器以保护隐私且高效的方式计算频率矩？事实上，正如 PI 在题为“零一频率定律”的论文中所表明的那样，这给我们带来了一个更有趣的问题：如何对所有可以通过流式秘密共享数据进行私密计算的函数进行分类。经过我们的目标是为流大量数据的隐私保护分析问题提供新的工具，特别是在 NSF 互联网测量研究征集提案的 MM-1B 部分中提到的。该项目还将允许以保护隐私的方式在小内存中计算所有聚合统计数据的大规模计算，并将 MPC 在流数据上的最先进性能提升到互联网大小的流。计算。我们的提案呼吁对研究生和本科生进行强有力的培训，包括积极寻找少数族裔和女学生进入密码学研究。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。