| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | October 31, 2016 |
| Latest Amendment Date: | July 18, 2018 |
| Award Number: | 1700657 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Nina Amla
namla@nsf.gov (703)292-7991 CNS Division Of Computer and Network Systems CSE Direct For Computer & Info Scie & Enginr |
| Start Date: | August 19, 2016 |
| End Date: | August 31, 2019 (Estimated) |
| Total Intended Award Amount: | $244,487.00 |
| Total Awarded Amount to Date: | $244,487.00 |
| Funds Obligated to Date: |
FY 2017 = $82,774.00 FY 2018 = $84,705.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
101 COMMONWEALTH AVE AMHERST MA US 01003-9252 (413)545-0698 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
MA US 01003-9242 |
| Primary Place of Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Secure &Trustworthy Cyberspace |
| Primary Program Source: |
01001718DB NSF RESEARCH & RELATED ACTIVIT 01001819DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The proliferation and increasing sophistication of censorship warrants continuing efforts to develop tools to evade it. Yet, designing effective mechanisms for censorship resistance ultimately depends on accurate models of the capabilities of censors, as well as how those capabilities will likely evolve. In contrast to more established disciplines within security, censorship resistance is relatively nascent, not yet having solid foundations for understanding censor capabilities or evaluating the effectiveness of evasion technologies. Consequently, the censorship resistance tools that researchers develop may ultimately fail to serve the needs of citizens who need them to communicate. Designers of these tools need a principled foundation for reasoning about design choices and tradeoffs.
To provide such a foundation, this project develops a science of censorship resistance: principled approaches to understanding the nature of censorship and the best ways to facilitate desired outcomes. The approach draws upon empirical studies of censorship as the foundation for models and abstractions to allow us to reason about the censorship-resistant technologies from first principles. The project aims to characterize and model censorship activities ranging from blocked search results to interference with international network traffic. The research develops theoretical models of censorship; reconciles these with large-scale empirical measurements; and uses these observations to design censorship-resistance tools to deploy in practice, as both components of Tor and standalone systems.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This award funded a multi-institutional collaboration to advance the state of the art in the science of understanding Internet censorship and online information controls. At the University of Massachusetts, this grant funded work on the ICLab network measurement platform that has enabled a unique global and longitudinal view of Internet censorship. A paper describing the tool, techniques, and relevant data analysis will appear in the 2020 IEEE Symposium on Privacy and Security.
This award, also work on information controls more broadly. Specifically, it funded research to understand how ISPs shape network traffic. Traffic shaping has been at the heart of the ``network neutrality'' debate which aims to define whether or not networks can reduce the transfer rates of different types of network traffic. While the issue of network neutrality remains hotly contested, our work illuminates how traffic shaping is used and deployed by networks at present. This view can help inform policy makers as they debate these issues (and indeed this work has been discussed by state-level regulators within the US as well as by regulators internationally in France). The results of this study appear in the ACM SIGCOMM 2019 conference.
This proposal funded PhD students at the University of Massachusetts as well as the PI to advise and mentor their work. Students that have worked on this project have written doctoral theses, and published papers in many top networking conferences including, IEEE Symposium on Internet and Privacy (OAKLAND) 2020, ACM SIGCOMM 2019, ACM Internet Measurement Conference 2016-2018, and ACM CoNEXT 2017. The results of this grant have also been used as a base for a course on Internet censorship taught by the PI at the University of Massachusetts--Amherst.
Last Modified: 12/30/2019
Modified by: Phillipa Gill
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