Unifying deep learning and knowledge representation for cybersecurity applications.

企业越来越多地产生更多的数据。分享和分析这些数据的价值越来越大。然而，它往往包含个人和敏感信息，这是非常重要的，以保护客户的隐私，不与第三方共享这些信息。这就导致了第22条军规。公司有他们想分享的数据，但他们不能分享，因为数据中的内容。自动化工具可用于分析数据和编辑敏感信息。然而，他们目前不能依靠，因为他们不够准确。这是因为准确的分类通常取决于对上下文的理解，这是计算机系统难以解决的问题。该项目旨在设计一种新型的自动分类系统，该系统融合了拓扑，上下文数据分析和自然语言处理算法。该系统将能够理解现有自动化系统无法处理的结构和上下文信息。这将允许更准确地识别个人和敏感信息，这反过来又将允许公司从其数据中共享和提取价值。

Companies are increasingly generating more data. There’s increasing value in sharing and analysing this data. However, it often contains personal and sensitive information and it’s very important to protect customer privacy by not sharing this information with third parties. This leads to a catch 22 situation. Companies have data they want to share but they can’t because of what’s in the data. Automated tools can be used to analyse the data and redact the sensitive information. However, they can’t currently be relied on because they’re not accurate enough. This is because accurate classification often depends on understanding context, which is something that computer systems struggle with. This project aims to design a new type of automated classification system that fuses best in class topological, contextual data analysis and natural language processing algorithms. This system will be able to understand both the structural and contextual information that existing automated systems can’t handle. This will allow personal and sensitive information to be identified much more accurately, which in turn will allow companies to share and extract value from their data.