CAREER: Sustainable Censorship Resistance Systems for the Next Decade

职业：未来十年可持续的审查制度抵抗系统

• 批准号: 1553301
• 负责人: Amir Houmansadr
• 金额: $ 58.15万
• 依托单位: University of Massachusetts Amherst
• 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=1553301&HistoricalAwards=false
• 关键词: CAREER; Sustainable; Censorship; Resistance; Systems

The Internet enables people around the world to communicate, fostering free speech, a free press, and democracy. For billions of people, however, the freedom to communicate via the Internet is regulated, monitored and restricted by governments or corporations. To combat such censorship, researchers have designed and deployed a variety of censorship circumvention systems. Unfortunately, such systems have been designed based on ad hoc heuristics (rather than on solid, theoretical foundations) and can be defeated by typical state-level censors. This research project is developing theory, methodologies, and tools to formally design, evaluate, and compare censorship resistance systems. The researchers are developing a formal model for the Internet censorship problem based on theoretical foundations. In particular, they are deriving a standard set of metrics for modeling the parties involved in each Internet censorship scenario, and leverage game theory, network science, and information theory for the analysis of specific censorship resistance systems. An important part of this project is the design and deployment of effective censorship resistance systems that will provide reliable, sustainable censorship resistance to Internet users. One approach being targeted by the researchers is leveraging emerging communication paradigms such as content caching, mobility, and cloud computing in the design of censorship circumvention tools. The second approach targeted by the researchers is adapting the design of circumvention systems to the architectural evolutions of the Internet. Specifically, the researchers are investigating the impacts of software-defined networking (SDN) and network functions virtualization (NFV) on Internet censorship and adapting the design of circumvention systems to ensure sustainable circumvention performance for the future.

互联网使世界各地的人们能够交流，促进言论自由，新闻自由和民主。 然而，对于数十亿人来说，通过互联网进行通信的自由受到政府或公司的监管、监视和限制。 为了对抗这种审查，研究人员设计并部署了各种审查规避系统。不幸的是，这样的系统是基于临时的审查（而不是基于坚实的理论基础）设计的，并且可以被典型的国家级审查所击败。该研究项目正在开发理论，方法和工具，以正式设计，评估和比较审查抵抗系统。研究人员正在开发一个基于理论基础的互联网审查问题的正式模型。特别是，他们正在推导一套标准的度量标准，用于对每个互联网审查场景中涉及的各方进行建模，并利用博弈论、网络科学和信息论来分析特定的审查抵抗系统。该项目的一个重要部分是设计和部署有效的抵制审查制度，为互联网用户提供可靠、可持续的抵制审查制度。研究人员针对的一种方法是利用新兴的通信范式，如内容缓存，移动性和云计算来设计审查规避工具。研究人员针对的第二种方法是使规避系统的设计适应互联网的架构演变。具体来说，研究人员正在调查软件定义网络（SDN）和网络功能虚拟化（NFV）对互联网审查的影响，并调整规避系统的设计，以确保未来可持续的规避性能。

项目成果

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning

回到绘图板：对生产联邦学习的中毒攻击的批判性评估

• DOI: 10.1109/sp46214.2022.9833647
• 发表时间: 2022
• 期刊: 2022 IEEE Symposium on Security and Privacy (SP
• 作者: Shejwalkar, Virat;Houmansadr, Amir;Kairouz, Peter;Ramage, Daniel
• 通讯作者: Ramage, Daniel

Machine Learning with Differentially Private Labels: Mechanisms and Frameworks

• DOI: 10.56553/popets-2022-0112
• 发表时间: 2022-10
• 期刊: Proc. Priv. Enhancing Technol.
• 作者: Xinyu Tang;Milad Nasr;Saeed Mahloujifar;Virat Shejwalkar;Liwei Song;Amir Houmansadr;Prateek

FINN: Fingerprinting Network Flows using Neural Networks

FINN：使用神经网络对网络流进行指纹识别

• DOI: 10.1145/3485832.3488010
• 发表时间: 2021
• 期刊: Annual Computer Security Applications Conference
• 作者: Rezaei, Fatemeh;Houmansadr, Amir
• 通讯作者: Houmansadr, Amir