Mattia Gustarini

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

Abstract 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.

Ubiquitous inference of mobility state of human custodian in people-centric context sensing

People-centric sensing using peoples smartphones offers new research opportunities for large case studies. It presents many challenges, e.g., efficient capture of persons mobility, understanding of context changes and preservation of user privacy. We propose an accurate and energy-efficient method able to capture users mobility, thus the context changes, while preserving his/her privacy. Our solution can be applied to systems that aim to efficiently sense context on smartphones to study large scale phenomena or perform location management.

Differences in smartphone usage: Validating, evaluating, and predicting mobile user intimacy

We analyze the users’ intimacy to investigate the differences in smartphone usage, considering the user’s location and number and kind of people physically around the user. With a first user study we (1) validate the intimacy concept, (2) evaluate its correlation to smartphone usage features and (3) we computationally model it. Shorter, more frequent, and less engaging interactions take place when intimacy is lower, while longer, less frequent, and engaging interactions when intimacy is higher. With a second user study, we investigate the intimacy predictability in practice. Location–time features are predictive for the intimacy, and other smartphone-based features can improve the intimacy prediction accuracy.

Smartphone Interactions Change for Different Intimacy Contexts

Emerging mobile applications on a growing scale adapt their functionalities and the way these are provided, leveraging the user’s contextual information, without the need of explicit settings setup from the users side. However, this contextual information, e.g., location and other environmental information, may not fully represent the users’ context. There are other contextual features related to the user’s social context that may be considered. In this paper we introduce an example of such contextual information - the intimacy context information, and we investigate if smartphone users change the way they interact with their smartphones depending on their intimacy state. We performed a 4 weeks 20-users study, with participants using their own smartphones in daily life environments, and being sampled for their intimacy perception. Our results show how intimacy context changes and relate to the smartphone usage. Therefore our research contributes by introducing new context information - intimacy, which can be leveraged by developers to create mobile applications automatically adapting to provide different services, functionalities and content depending on the intimacy level of the situation the user is in.