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https://gm-neurips-2020.github.io/
在这次演讲中，Graph Mining team的创始人Vahab对本图挖掘和学习进行了高层次的介绍。这个演讲涉及到什么是图，为什么它们是重要的，以及它们在大数据世界中的位置。然后讨论了组成图挖掘和学习工具箱的核心工具，并列出了几个规范的用例。它还讨论了如何结合算法、系统和机器学习来在不同的分布式环境中构建一个可扩展的图学习系统。最后，它提供了关于Google一个简短的历史图挖掘和学习项目。本次演讲将介绍接下来的演讲中常见的术语和主题。
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【312页ppt】Mining and Learning with Graphs at Scale
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Single sign-on authentication systems such as OAuth 2.0 are widely used in web services. They allow users to use accounts registered with major identity providers such as Google and Facebook to login on multiple services (relying parties). These services can both identify users and access a subset of the user's data stored with the provider. We empirically investigate the end-user privacy implications of OAuth 2.0 implementations in relying parties most visited around the world. We collect data on the use of OAuth-based logins in the Alexa Top 500 sites per country for five countries. We categorize user data made available by four identity providers (Google, Facebook, Apple and LinkedIn) and evaluate popular services accessing user data from the SSO platforms of these providers. Many services allow users to choose from multiple login options (with different identity providers). Our results reveal that services request different categories and amounts of personal data from different providers, with at least one choice undeniably more privacy-intrusive. These privacy choices (and their privacy implications) are highly invisible to users. Based on our analysis, we also identify areas which could improve user privacy and help users make informed decisions.
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