Unifying deep learning and knowledge representation for cybersecurity applications.

企业产生的数据越来越多。分享和分析这些数据的价值越来越大。然而，它通常包含个人和敏感信息，并且通过不与第三方共享这些信息来保护客户隐私非常重要。这就导致了进退两难的局面。公司有他们想要分享的数据，但由于数据中的内容，他们不能分享。自动化工具可用于分析数据和编辑敏感信息。然而，它们目前还不可靠，因为它们不够准确。这是因为准确的分类通常依赖于对上下文的理解，这是计算机系统难以做到的。该项目旨在设计一种新型的自动分类系统，该系统融合了最佳的类拓扑，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。这将使个人和敏感信息被更准确地识别出来，从而使公司能够分享并从他们的数据中提取价值。

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.