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CRII: CIF: Distributed Computing with Privacy and Security Considerations

CRII：CIF：考虑隐私和安全的分布式计算

基本信息

批准号：
1849757
负责人：
Martina Cardone
金额：
$ 17.5万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2022-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849757&HistoricalAwards=false
关键词：
CRII CIF Distributed Computing Privacy

项目摘要

Modern datasets have reached such sizes and complexities as to make them difficult to analyze and process using traditional centralized approaches. Distributed computing enables large-scale data processing, by distributing the effort of a computing task over such datasets across a network of machines that operate in parallel. With the proliferation of sensitive data that needs to be processed, and the ever-growing computational power of adversarial parties, ensuring data privacy and security of the computing process represents a critical aspect for the design of modern distributed computing systems. This project aims at developing a theoretical foundation to investigate the performance of distributed computing in the presence of machines that are not trusted or well-protected. This project will promote undergraduate and graduate research, and the outcomes of the proposed research will be integrated into education.This project considers two different distributed computing scenarios and seeks their theoretical performance limits. In the first scenario, the dataset contains sensitive information (such as clinical/genome information) and, when distributed, has to remain confidential from the machines. An optimization framework is used which seeks to quantify the trade-off between the performance of the computing task and the level of data privacy that can be guaranteed. In the second scenario, the machines may have been compromised and hence may introduce false information to the system. The main focus is on the design of distributed computing techniques that are robust to different message-tampering attacks and allow one to efficiently identify the attacked machines.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.

现代数据集已经达到了这样的规模和复杂性，使得它们难以使用传统的集中方法进行分析和处理。分布式计算通过将计算任务的工作量分布在并行运行的机器网络上的数据集上，从而实现了大规模的数据处理。随着需要处理的敏感数据的激增，以及敌对各方计算能力的不断增长，确保计算过程的数据隐私和安全是现代分布式计算系统设计的一个关键方面。该项目旨在发展一个理论基础，以研究分布式计算在不受信任或保护良好的机器存在下的性能。此计划将促进本科生和研究生的研究，并将研究成果纳入教育。这个项目考虑了两种不同的分布式计算场景，并寻求它们的理论性能极限。在第一种情况下，数据集包含敏感信息（如临床/基因组信息），并且在分发时必须对机器保持机密。使用了一个优化框架，该框架旨在量化计算任务的性能和可以保证的数据隐私级别之间的权衡。在第二种情况下，机器可能已经被破坏，因此可能会向系统引入虚假信息。重点是分布式计算技术的设计，这些技术对不同的消息篡改攻击具有鲁棒性，并允许人们有效地识别受攻击的机器。该奖项反映了美国国家科学基金会的法定使命，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。