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Anonymous smartphone data collection: factors influencing the users' acceptance in mobile crowd sensing

机译:匿名智能手机数据收集:影响用户在移动人群感知中的接受度的因素

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Mobile crowd sensing (MCS) assumes a collaborative effort from mobile smartphone users to sense and share their data needed to fulfill a given MCS objective (e.g., modeling of urban traffic or wellness index of a community). In this paper, we investigate the user's perception of anonymity in MCS and factors influencing it. We conducted a 4-week extensive smartphone user study to fulfill three main objectives. (1) Understand if users prefer to share data anonymously or not anonymously. (2) Investigate the possible factors influencing the difference between these two modalities, considering: (a) users' sharing attitude, (b) shared data kind and (c) users' intimacy when data are shared (we defined intimacy as the users' perception of their context with respect to place, number and kind of people around them). (3) Identify further users' personal factors influencing their perception of anonymity via multiple interviews along the user study. In the results, we show that data are shared significantly more when anonymously collected. We found that the shared data kind is the factor significantly contributing to this difference. Additionally, users have a common way to perceive anonymity and its effectiveness. To ensure the success of anonymization algorithms in the context of MCS systems, we highlight which issues the researchers developing these algorithms should carefully consider. Finally, we argue about new research paths to better investigate the user perception of anonymity and develop anonymous MCS systems that users are more likely to trust based on our findings.
机译:移动人群感知(MCS)假定了移动智能手机用户的共同努力,以感知和共享实现既定MCS目标(例如,城市交通建模或社区健康指数)所需的数据。在本文中,我们调查了用户对MCS中匿名性的感知以及影响匿名性的因素。我们进行了为期4周的智能手机用户广泛研究,以实现三个主要目标。 (1)了解用户是喜欢匿名共享还是不匿名共享数据。 (2)研究影响这两种方式之间差异的可能因素,考虑以下因素:(a)用户的共享态度,(b)共享的数据种类和(c)数据共享时的用户亲密感(我们将亲密感定义为用户的亲密感关于他们周围的位置,人数和种类的背景信息)。 (3)通过在用户研究过程中进行多次访谈,进一步确定用户的个人因素,从而影响他们对匿名的看法。结果显示,匿名收集数据时,共享的数据明显更多。我们发现共享数据类型是造成这种差异的重要因素。此外,用户还有一种通用的方式来感知匿名及其有效性。为了确保匿名化算法在MCS系统环境中的成功,我们着重指出开发这些算法的研究人员应仔细考虑的问题。最后,我们讨论了新的研究路径,以更好地调查用户对匿名的感知,并根据我们的发现开发用户更可能信任的匿名MCS系统。

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