CIF: NeTS: Medium: Collaborative Research: Unifying Data Synchronization

CIF：NetTS：媒介：协作研究：统一数据同步

• 批准号: 1563710
• 负责人: Michael Mitzenmacher
• 金额: $ 40万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563710&HistoricalAwards=false
• 关键词: CIF; NeTS; Medium; Collaborative; Research

This project addresses the technical means of maintaining and sharing the huge amounts of diverse data that are being generated from the computing devices that increasingly envelop everyday lives, specifically by improving synchronization technology. The goal is to synthesize various approaches that have been developed, often in an ad hoc manner and in various fields, into a general, holistic solution that includes benefits from new analyses and solutions. The approach is fundamental in nature, and its effectiveness shows promise for impact on a diverse set of technologies, including mobile and cloud computing, digital currency, security, and biological sequencing. Part of the work involves outreach to younger students, such as initial efforts at setting up a novel computing-based summer program for high school students.The project focuses on four research thrusts. The first involves an analysis, comparison, and improvement (where possible) of core point-to-point synchronization primitives from the existing literature. The second thrust generalizes these primitives to higher-dimensional data, such as files, images, video, databases, and graphs. The third research thrust involves developing approaches for synchronizing data from many synchronizing parties communicating through a variety of channels. Finally, the project aims to implement a general data synchronization engine based on the research results, together with an evaluation of various applications.

该项目致力于维护和共享从日益覆盖日常生活的计算设备生成的大量不同数据的技术手段，特别是通过改进同步技术。 其目标是将往往以临时方式在各个领域制定的各种办法综合成一个全面的综合解决办法，其中包括从新的分析和解决办法中获益。 这种方法本质上是基础性的，其有效性显示出对各种技术的影响，包括移动的和云计算、数字货币、安全和生物测序。 该项目的部分工作涉及到对年轻学生的拓展，例如为高中生建立一个新颖的基于计算的暑期项目的初步努力。 第一个涉及到分析，比较，并改善（在可能的情况下）的核心点到点同步原语从现有的文献。 第二个推力将这些原语推广到更高维的数据，如文件、图像、视频、数据库和图形。 第三个研究重点涉及开发方法，用于同步通过各种渠道进行通信的多个同步方的数据。 最后，本计画的目标是在研究结果的基础上，实作一个通用的资料同步引擎，并对各种应用进行评估。

项目成果

Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix

• 发表时间: 2021-06
• 作者: Maximilian Lam;Gu-Yeon Wei;D. Brooks;V. Reddi;M. Mitzenmacher

Putting the "Learning" into Learning-Augmented Algorithms for Frequency Estimation

• 发表时间: 2021
• 作者: Elbert Du;Franklyn Wang;M. Mitzenmacher

Queues with Small Advice

带有小建议的队列

• 发表时间: 2021
• 期刊: SIAM Conference on Applied and Computational Discrete Algorithms
• 作者: Mitzenmacher, Michael