In this paper, we explore the challenges of ensuring security and privacy for
users from diverse demographic backgrounds. We propose a threat modeling
approach to identify potential risks and countermeasures for product inclusion
in security and privacy. We discuss various factors that can affect a user's
ability to achieve a high level of security and privacy, including low-income
demographics, poor connectivity, shared device usage, ML fairness, etc. We
present results from a global security and privacy user experience survey and
discuss the implications for product developers. Our work highlights the need
for a more inclusive approach to security and privacy and provides a framework
for researchers and practitioners to consider when designing products and
services for a diverse range of users.

我们探讨了确保来自不同人口背景的用户的安全和隐私的挑战，并提出了一种威胁建模方法，以识别产品安全和隐私的潜在风险和对策。我们讨论了影响用户实现高水平安全和隐私的各种因素，包括低收入人口、连接质量差、共用设备使用、ML 公平性等。我们展示了一项全球安全和隐私用户体验调查的结果，并讨论了对产品开发者的影响。我们的工作突出了安全和隐私需要更具包容性的方法，并为研究人员和从业者设计面向各类用户的产品和服务提供了一个框架。

安全和隐私产品融入

Security and Privacy Product Inclusion

Natural language processing researchers develop models of grammar, meaning
and human communication based on written text. Due to task and data
differences, what is considered text can vary substantially across studies. A
conceptual framework for systematically capturing these differences is lacking.
We argue that clarity on the notion of text is crucial for reproducible and
generalizable NLP. Towards that goal, we propose common terminology to discuss
the production and transformation of textual data, and introduce a two-tier
taxonomy of linguistic and non-linguistic elements that are available in
textual sources and can be used in NLP modeling. We apply this taxonomy to
survey existing work that extends the notion of text beyond the conservative
language-centered view. We outline key desiderata and challenges of the
emerging inclusive approach to text in NLP, and suggest systematic
community-level reporting as a crucial next step to consolidate the discussion.

本研究旨在确定自然语言处理中所谓文本的概念，并提出一套通用术语和分类体系，以扩展文本的范围，从而更好地进行 NLP 建模。该研究强调了包容性方法在 NLP 中处理文本的重要性，并呼吁开发者采用系统化的报告方式，以巩固该话题。