Privacy-by-Design (PbD) suggests designing the fundamental architecture and features of computing systems with privacy in mind. Although widely adopted by regulatory frameworks, a growing number of critics have questioned whether PbD's focus on compliance with privacy regulation may prevent it from addressing users' specific privacy attitudes and expectations. Motivated to enhance user-centered privacy-by-design processes, we examine what are the consequences of the way privacy questions are framed to crowd users, and how personal characteristics of the crowd users impact their responses. We recruited a total of 665 participants, of which 456 were recruited via Amazon Mechanical Turk (AMT), and 209 were university students. We show that the framing of computing systems' features using data flows results in features' evaluations that are less critical, compared to using descriptions of personal experiences. We also found, based on the student sample, that students with professional engineering experience are less critical than those with no work experience when assessing the features' appropriateness. We discuss how our results can be used to enhance privacy-by-design processes and encourage user-centered privacy engineering.

Privacy-by-Design (PbD) 建议在设计计算系统的基本架构和功能时考虑到隐私。尽管被监管框架广泛采用，但越来越多的批评者质疑 PbD 对遵守隐私监管的关注是否会阻止其解决用户特定的隐私态度和期望。出于增强以用户为中心的隐私设计流程的动机，我们研究了将隐私问题设置为人群用户的方式的后果，以及人群用户的个人特征如何影响他们的回答。我们总共招募了 665 名参与者，其中 456 名是通过 Amazon Mechanical Turk (AMT) 招募的，209 名是大学生。我们表明，使用数据流对计算系统的特征进行框架化会导致特征“ 与使用个人经历的描述相比，评估不那么重要。我们还发现，根据学生样本，在评估功能的适当性时，具有专业工程经验的学生不如没有工作经验的学生那么挑剔。我们讨论了如何使用我们的结果来增强隐私设计流程并鼓励以用户为中心的隐私工程。