TWC: Small: Critter@home: Content-Rich Traffic Trace Repository from Real-Time, Anonymous, User Contributions

TWC：小型：Critter@home：来自实时、匿名、用户贡献的内容丰富的流量跟踪存储库

• 批准号: 1224035
• 负责人: Jelena Mirkovic
• 金额: $ 37.5万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1224035&HistoricalAwards=false
• 关键词: TWC; Small; Critter; home; Content

There are very few publicly available network traces that contain application-level data, because of the enormous privacy risk that sharing such data creates. Application-level data is rich with personal and private information, such as human names, social security numbers, etc. that criminals can monetize. Yet such data is necessary for realistic testing of research products, and for understanding trends in the domain of networking and network applications.This project develops a publicly accessible, diverse and fresh archive of content-rich network data, contributed by volunteer users, called Critter-at-home. Users join the Critter overlay whenever online, offering their data to interested researchers. Privacy of data contributors is protected by several means. First, contributors may opt to host their own data on their machines, thus retaining full control over it. Second, we process contributed data to modify all personal and private information (PPI) and we encrypt it. Third, no human apart from the contributor ever accesses the raw, PPI-sanitized, data. Instead, researchers query the data via our Critter-at-home framework, and they receive aggregate statistics (counts, distributions, etc.) of the traffic features they query for. Four, all contact with a contributor is at her discretion and is done through an anonymous network, where contributor identities are hidden.The archive this project creates will greatly advance security research by providing necessary data for its validation and for data mining. This archive will further be valuable to a broader networking e.g., for realistic traffic generation, as ground truth in traffic classification, and for many other purposes.

很少有公开的网络跟踪包含应用程序级数据，因为共享此类数据会带来巨大的隐私风险。应用程序级数据包含丰富的个人和私人信息，如姓名、社会安全号码等，犯罪分子可以从中获利。然而，这些数据是必要的研究产品的现实测试，并了解在网络和网络应用领域的趋势。该项目开发了一个公开访问的，多样化的和新鲜的档案内容丰富的网络数据，由志愿者用户贡献，称为Critter-at-home。用户只要在线就可以加入Critter覆盖层，将他们的数据提供给感兴趣的研究人员。数据贡献者的隐私受到多种方式的保护。首先，贡献者可以选择将自己的数据托管在自己的机器上，从而保留对数据的完全控制权;其次，我们处理贡献的数据以修改所有个人和私人信息（PPI），并对其进行加密;第三，除了贡献者之外，没有人可以访问经过PPI消毒的原始数据。相反，研究人员通过我们的Critter-at-home框架查询数据，他们会收到汇总统计数据（计数，分布等）。他们所查询的交通特征。第四，所有与贡献者的联系都由她自行决定，并通过匿名网络进行，贡献者身份被隐藏。该项目创建的档案将通过提供必要的数据来验证和数据挖掘，从而大大推进安全研究。该档案将进一步对更广泛的网络有价值，例如，用于实际业务生成、作为业务分类中的基础事实以及用于许多其他目的。