SaTC: CORE: Small: Toward Fully Automated Data-Driven Analysis of Web Censorship

不同的国家在言论自由方面的立场非常不同，可能会越来越多地使用在线过滤工具进行网络审查。虽然研究界对匿名通信问题给予了极大的关注，但审查和匿名化的许多其他方面还没有得到详细的研究。例如，我们不知道是否有可能自动推断审查者使用的是什么技术，审查者想要的是什么主题。该项目以经验测量为基础，首先旨在制作全球范围内实际网络审查实践中使用的技术和可疑审查目标的量化模型。在这些测量的启发下，第二个目标是设计新的技术，在最大限度地减少人工参与的情况下执行自动审查数据收集。该小组将把项目成果和技术纳入课程，并通过公开提供测量数据，为计算机安全领域(例如社会学或政治学)以外领域的研究人员以及对言论自由问题感兴趣的非政府组织和政策制定者提供有用的资源。在技术层面，该项目将确定和建立可靠和有代表性的网页语料库，以测试审查制度，并收集全球范围内和在很长一段时间内审查情况的衡量标准。一个关键目标将是尽量减少数据收集过程中的人的参与，以减少对个人的风险，同时仍然寻求从代表各种国家和组织的各种有利地点收集数据。这将涉及开发改进请求地理位置的技术，将基于域名服务的测量技术与虚拟专用网络和服务器的使用相结合以扩大覆盖范围，避免在进行这些测量时被审查者发现，开发将网络或服务器错误与审查行为区分开来的技术，解决跨语言访问和审查问题，并考虑到审查者行为随着时间的推移而发生的变化。该团队将分析这些数据，以确定是否可以在对审查可能采取的形式做出最小假设的情况下自动检测到审查的发生，以及是否可以自动提取被审查材料的可能主题。根据这些关于被审查主题的推论，该团队将调查基于关键字的审查在野外的程度，以及实际审查做法如何与审查者本身和其他监督审查组织对审查行为的公开描述保持一致。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Different countries have very different positions concerning freedom of expression, and may be increasingly using online filtering tools for web censorship. While the research community has devoted significant attention to the problem of anonymous communication, many other aspects of censorship and anonymization have not yet been studied in detail. We do not know, for instance, whether it is possible to automatically infer which techniques censors use and which topics censors are after. Grounded in empirical measurements, this project first aims to produce quantitative models of the techniques used in practice for actual web censorship on a global scale, and of the suspected targets of censorship. Informed by these measurements, a secondary goal is to devise novel techniques to perform automated censorship data collection with minimal human involvement. The team will integrate project findings and techniques into coursework, and, by making measurement data publicly available, will provide a useful resource to researchers in fields beyond the realm of computer security (for example, sociology or political science), as well as non-governmental organizations and policy-makers interested in freedom of speech issues. At a technical level, the project will identify and construct reliable and representative corpora of web pages to test for censorship, and gather measurements of censorship occurrences on a global scale and over long intervals of time. A key objective will be to minimize human involvement during data collection to reduce risk to the individuals, while still seeking to collect data from a wide mix of vantage points representing a variety countries and organizations. This will involve developing techniques for improving geolocation of requests, combining domain name service-based measurement techniques with the use of virtual private networks and servers to increase coverage, avoiding detection by censors while taking these measurements, developing techniques to distinguish network or server errors from acts of censorship, addressing issues of cross-language access and censorship, and accounting for changes in censors' behavior over time. The team will analyze this data to determine whether one can automatically detect the occurrence of censorship with minimal assumptions about the form censorship may take, and whether one can automatically extract the probable topic of the censored material. Informed by these inferences around censored topics, the team will investigate the extent of keyword-based censorship in the wild, and how actual censoring practice aligns with published accounts of censor behavior from both censors themselves and from other organizations that monitor censorship.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.