TWC: Medium: Collaborative: Capturing People's Expectations of Privacy with Mobile Apps by Combining Automated Scanning and Crowdsourcing Techniques

TWC：媒介：协作：结合自动扫描和众包技术，利用移动应用捕捉人们对隐私的期望

• 批准号: 1228813
• 负责人: Jason Hong
• 金额: $ 80万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1228813&HistoricalAwards=false
• 关键词: TWC; Medium; Collaborative; Capturing; People

The goal of our work is to (a) capture people's expectations and surprises in using mobile apps in a scalable manner, and to (b) summarize these perceptions in a simple format to help people make better trust decisions. Our main idea is analyzing privacy in the form of people's expectations about what an app will and won't do, focusing on where an app breaks people's expectations. We are building an App Scanner that combines automated scanning techniques with crowdsourcing. Automated scanning captures the behavior of an app, while crowdsourcing is used to interpret how expected and acceptable this behavior is. This information is used as the basis for building a better privacy summary for apps. We have organized an interdisciplinary team with expertise in mobile computing, computer security, systems, and human-computer interaction.Success in this work will include results in: (a) the design and implementation of an App Scanner that combines automated techniques with crowdsourcing techniques for analyzing and interpreting privacy-related behaviors of mobile apps; (b) a series of evaluations of this app scanner, showing effectiveness, accuracy, and scalability; (c) the design and evaluation of better privacy summaries, which prioritize and highlight the most unexpected behaviors of an app; and (d) demonstration of a new conceptualization of privacy, namely privacy as expectations. Success will also help end-users, corporate and government employees manage their privacy better than can be done today.

我们的工作目标是(A)捕捉人们在以可扩展的方式使用移动应用程序时的期望和惊喜，并(B)以简单的格式总结这些感知，以帮助人们做出更好的信任决定。我们的主要想法是以人们对应用程序会做什么和不会做什么的期望的形式来分析隐私，专注于应用程序在哪里打破了人们的期望。我们正在构建一个App Scanner，将自动扫描技术与众包相结合。自动扫描捕捉应用程序的行为，而众包则用来解释这种行为的预期程度和可接受性。这些信息被用作为应用程序构建更好的隐私摘要的基础。我们组织了一支在移动计算、计算机安全、系统和人机交互方面拥有专业知识的跨学科团队。这项工作的成功将包括：(A)设计和实现一个应用程序扫描程序，它将自动化技术与众包技术相结合，用于分析和解释移动应用程序的隐私相关行为；(B)对该应用程序扫描程序进行一系列评估，显示出有效性、准确性和可扩展性；(C)设计和评估更好的隐私摘要，其中优先考虑并突出应用程序最意外的行为；以及(D)演示隐私的新概念，即将隐私视为期望。成功还将帮助最终用户、企业和政府雇员比今天更好地管理他们的隐私。