SaTC: CORE: Medium: Collaborative: Automatically Answering People's Privacy Questions

SaTC：核心：媒介：协作：自动回答人们的隐私问题

• 批准号: 1914486
• 负责人: Norman Sadeh
• 金额: $ 61.24万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914486&HistoricalAwards=false
• 关键词: SaTC; CORE; Medium; Collaborative; Automatically

As novel technologies collect increasingly large and diverse amounts of data about us, people are unable to keep up and retain control over what happens to their data. The current legal approach to privacy concentrates on the concept of "Notice and Choice", namely the expectation that people are provided sufficient information about the collection and use of their data, and are offered meaningful choices about these practices (e.g., opt out, opt in). A primary element of this approach relies on privacy policies to communicate this information to people. In practice, these policies tend to be long, vague and ambiguous. Not too surprisingly, few people find the time to read them and those who do often struggle to understand what they say. This multi-disciplinary project aims to develop novel technology that will enable people to regain a sense of control by enabling them to simply ask questions about the privacy issues that matter to them rather than requiring them to read long, one-size-fits all privacy policies. In addition to producing new knowledge and technologies and contributing to improving the state of privacy in the United States, this project will also create education and research opportunities for both undergraduate and graduate students at participating universities, including activities to broaden participation of women and under-represented minorities in this important area of computer science, and contribute to the development of technologies with the potential to help the visually impaired take advantage of information found in the text of privacy policies.This multi-disciplinary project builds on recent advances in natural language processing, machine learning, code analysis and user modeling to re-invent notice and choice, moving from long and hard-to-understand notices to interactive privacy dialogues with users. An important part of this research involves the development of question answering functionality that enables users to ask questions about those issues that truly matter to them rather than presenting them with one-size-fits-all privacy notices. Another involves supplementing disclosures found in privacy policies with additional sources of information such as background knowledge (e.g. knowledge about common data practices and relevant laws) and code analysis to disambiguate statements and provide additional details to users when it matters (e.g., with whom their data is actually shared). This research will be guided by user-centered design methodologies where the design of novel technologies is informed by findings from human subject studies, and where technologies are deployed and evaluated in increasingly rich and realistic scenarios. Products of this research will include prototype privacy Question Answering functionality, as well as technology to automatically extract information about data collection and use practices from both the text of privacy policies and from code.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着新型技术收集越来越大，越来越多的数据，人们无法跟上并保留对数据发生的事情的控制。当前对隐私的法律方法集中在“通知和选择”的概念上，即对人们提供有关收集和使用数据的足够信息的期望，并就这些实践提供了有意义的选择（例如，选择退出，选择）。这种方法的主要要素依赖于隐私政策将这些信息传达给人们。实际上，这些政策往往很长，模糊和模棱两可。 并不是太奇怪了，很少有人找到时间阅读它们，而那些经常努力理解他们说的话的人。这个多学科项目旨在开发新型技术，使人们能够通过使他们能够简单地提出有关对他们重要的隐私问题的问题来重新获得控制感，而不是要求他们阅读长时间的一定大小适合所有隐私政策。除了生产新的知识和技术并为改善美国的隐私状态做出贡献外，该项目还将为参与大学的本科生和研究生创造教育和研究机会，包括妇女的活动，包括妇女的参与以及占主导地位的少数群体在这项重要的计算机科学领域中的发展，并在计算机科学领域中促进技术的发展，并为无效的技术提供了贡献，以实现不知不线的发展，以实现对技术的发展，以实现不知不觉中的信息，以实现不知不线的信息。多学科项目基于自然语言处理，机器学习，代码分析和用户建模的最新进展，以重新发明通知和选择，从漫长而难以理解的通知到与用户的交互式隐私对话。这项研究的重要组成部分是开发问题回答功能，使用户能够询问有关这些问题对他们真正重要而不是向他们介绍一定大小的隐私通知的问题。另一个涉及补充隐私政策中发现的披露，并使用其他信息来源，例如背景知识（例如，关于常见数据实践和相关法律的知识）和代码分析，以消除歧义陈述，并在重要的情况下为用户提供其他详细信息（例如，与他们的数据实际共享）。这项研究将由以用户为中心的设计方法来指导，在该方法中，新技术的设计通过人类学科研究的发现以及在越来越丰富且现实的场景中部署和评估的技术来告知。这项研究的产品将包括原型隐私问题答案功能，以及从隐私政策和法规文本中自动提取有关数据收集和使用实践信息的技术。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持的。

项目成果

Helping Mobile Application Developers Create Accurate Privacy Labels

帮助移动应用开发者创建准确的隐私标签

• DOI: 10.1109/eurospw55150.2022.00028
• 发表时间: 2022
• 期刊: IWPE
• 作者: Gardner, Jack;Feng, Yuanyuan;Reiman, Kayla;Lin, Zhi;Jain, Akshath;Sadeh, Norman
• 通讯作者: Sadeh, Norman

A Design Space for Privacy Choices: Towards Meaningful Privacy Control in the Internet of Things

• DOI: 10.1145/3411764.3445148
• 发表时间: 2021-01-01
• 期刊: CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS
• 作者: Feng, Yuanyuan;Yao, Yaxing;Sadeh, Norman
• 通讯作者: Sadeh, Norman

Question Answering for Privacy Policies: Combining Computational and Legal Perspectives

• DOI: 10.18653/v1/d19-1500
• 发表时间: 2019-11
• 期刊: ArXiv
• 作者: Abhilasha Ravichander;A. Black;Shomir Wilson;Thomas B. Norton;N. Sadeh

Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?

• DOI: 10.18653/v1/2021.acl-long.319
• 发表时间: 2021
• 作者: Abhilasha Ravichander;A. Black;Thomas B. Norton;Shomir Wilson;N. Sadeh

Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding

• DOI: 10.21437/interspeech.2021-1537
• 发表时间: 2021-06
• 期刊: 2022 RIVF International Conference on Computing and Communication Technologies (RIVF)
• 作者: Siddhant Arora;Alissa Ostapenko;Vijay Viswanathan;Siddharth Dalmia;Florian Metze;Shinji Watanabe;A. Black