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

包含应用程序级数据的公开可用的网络跟踪非常少，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，如人名、社会保险号等，犯罪分子可以从中获利。然而，这些数据对于研究产品的现实测试以及了解网络和网络应用领域的趋势是必要的。该项目开发了一个由志愿用户贡献的、可公开访问的、多样化的、新鲜的内容丰富的网络数据档案，称为Critter-at-Home。用户在任何时候在线都会加入Critter覆盖，将他们的数据提供给感兴趣的研究人员。数据贡献者的隐私通过几种方式得到保护。首先，贡献者可以选择在他们的机器上托管他们自己的数据，从而保持对它的完全控制。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，没有人会访问原始的、经过PPI消毒的数据。取而代之的是，研究人员通过我们的Critter-in-Home框架查询数据，他们获得汇总的统计数据(计数、分布等)。他们查询的交通特征。第四，与投稿人的所有联系都由她自行决定，并通过隐藏投稿人身份的匿名网络进行。该项目创建的档案将为其验证和数据挖掘提供必要的数据，从而极大地促进安全研究。该档案将进一步对更广泛的联网有价值，例如对于现实的业务生成、作为业务分类中的基本事实以及用于许多其他目的。

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.