Context Data Categories and Privacy Model for Mobile Data Collection Apps

Beierle, Felix; Tran, Vinh Thuy; Allemand, Mathias; Neff, Patrick; Schlee, Winfried; Probst, Thomas; Pryss, Rüdiger; Zimmermann, Johannes

doi:10.1016/j.procs.2018.07.139

Cited by 48 publications

(45 citation statements)

References 31 publications

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“…The more ambitious strategy would involve the development of new instruments that are specifically tailored to the assessment of personality states, traits, and life experiences over time, ideally involving complementary assessment methods such as informant reports (Oltmanns, Jackson, & Oltmanns, 2019; Vazire, 2006), interaction partner reported behaviour (Geukes et al, 2019), digital footprints of behaviour (Adjerid & Kelley, 2018; Bleidorn & Hopwood, 2019; Kosinski, Matz, Gosling, Popov, & Stillwell, 2015), mobile sensing of behaviour and contexts in real time (Beierle et al, 2018; Harari et al, 2017), narratives (Dunlop, 2015), behavioural tasks and observational measures (Back, Schmukle, & Egloff, 2009; Borkenau, Mauer, Riemann, & Spinath, 2004; Funder, Furr, & Colvin, 2000; Mihura, Meyer, Dumitrascu, & Bombel, 2013; Sadler, Ethier, Gunn, Duong, & Woody, 2009), and biological markers (Briley & Livengood, 2018). In addition to providing more reliable assessments of relevant constructs, the observation of discrepancies between different assessment methods may further lead to new insights regarding the sources and processes of personality change.…”

Section: Longitudinal Experience‐wide Association Studiesmentioning

confidence: 99%

Longitudinal Experience–Wide Association Studies—A Framework for Studying Personality Change

et al. 2020

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The importance of personality for predicting life outcomes in the domains of love, work, and health is well established, as is evidence that personality traits, while relatively stable, can change. However, little is known about the sources and processes that drive changes in personality traits and how such changes might impact important life outcomes. In this paper, we make the case that the research paradigms and methodological approaches commonly used in personality psychology need to be revised to advance our understanding of the sources and processes of personality change. We propose Longitudinal Experience‐Wide Association Studies as a framework for studying personality change that can address the limitations of current methods, and we discuss strategies for overcoming some of the challenges associated with Longitudinal Experience‐Wide Association Studies. © 2020 European Association of Personality Psychology

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Section: Longitudinal Experience‐wide Association Studiesmentioning

confidence: 99%

Longitudinal Experience–Wide Association Studies—A Framework for Studying Personality Change

et al. 2020

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“…In each study, participants were explicitly informed about the purpose of the data collection and consented to using the mobile sensing apps prior to participation. Our studies were approved by the appropriate ethics committees at each respective institution: S1 study approved by the Committee for Protection of In addition, we incorporated the following study design features in all four of our studies to protect participants' privacy while using the apps (see Beierle et al, 2018 for a through description of such considerations): (a) users consented to install the app and track their data, (b) users could opt-out at any point during the data collection period, (c) data were associated with random identifiers, (d) data were anonymized, (e) the app utilized the permission system, and (f) the data were securely transferred from the apps to our servers using SSL encryption.…”

Section: Ethics Approvalmentioning

confidence: 99%

Sensing sociability: Individual differences in young adults’ conversation, calling, texting, and app use behaviors in daily life.

Harari¹,

Müller²,

Stachl³

et al. 2020

Journal of Personality and Social Psychology

147

132

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Sociability as a disposition describes a tendency to affiliate with others (vs. be alone). Yet, we know relatively little about how much social behavior people engage in during a typical day. One challenge to documenting behavioral sociability tendencies is the broad number of channels over which socializing can occur, both in-person and through digital media. To provide an assessment of individual differences in everyday social behavior patterns, here we used smartphone-based mobile sensing methods (MSMs) in four studies (total N = 1078) to collect real-world data about the sensed social behaviors of young adults across four communication channels: conversations, phone calls, text messages, and messaging and social media application use. To examine individual differences, we first focused on establishing between-person variability in daily social behavior, examining stability of and relationships among daily sensed social behavior tendencies. To explore factors that may explain the observed individual differences in sensed social behavior, we then expanded our focus to include other time estimates (e.g., times of the day, days of the week) and personality traits. In doing so, we present the first large-scale descriptive portrait of behavioral sociability patterns, characterizing the degree of social behavior young adults typically engaged in and mapping behavioral to self-reported personality dispositions. Our discussion focuses on how the observed sociability patterns compare to previous research on young adults' social behavior. We conclude by pointing to areas for future research aimed at understanding sociability using mobile sensing and other naturalistic observation methods for the assessment of social behavior.

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“…Privacy-preserving similarity comparison can among others be performed on item vectors [4] as well as texting data [15]. • Context Data: Data that characterizes the encounter such as location, time, weather, or peer activity (running, eating, commuting) that can be sensed (for example via sensors) or retrieved (for example from the web) [6,30].…”

Section: Similarity Data Peer Preference List and Neighborhood Prefmentioning

confidence: 99%

“…Access to other peers' data is limited to the time of contact and amount of data individually made available by nearby peers. 6 Propagate and Filter is independent of any specific recommendation algorithm. The filtering techniques described in [2] are viable approaches leveraging contextual data.…”

Section: Privacy By Disconnectionmentioning

confidence: 99%

On gossip-based information dissemination in pervasive recommender systems

Eichinger

Beierle

Papke

et al. 2019

Proceedings of the 13th ACM Conference on Recommender Systems

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Pervasive computing systems employ distributed and embedded devices in order to raise, communicate, and process data in an anytime-anywhere fashion. Certainly, its most prominent device is the smartphone due to its wide proliferation, growing computation power, and wireless networking capabilities. In this context, we revisit the implementation of digitalized word-of-mouth that suggests exchanging item preferences between smartphones offline and directly in immediate proximity. Collaboratively and decentrally collecting data in this way has two benefits. First, it allows to attach for instance location-sensitive context information in order to enrich collected item preferences. Second, model building does not require network connectivity. Despite the benefits, the approach naturally raises data privacy and data scarcity issues. In order to address both, we propose Propagate and Filter, a method that translates the traditional approach of finding similar peers and exchanging item preferences among each other from the field of decentralized to that of pervasive recommender systems. Additionally, we present preliminary results on a prototype mobile application that implements the proposed device-to-device information exchange. Average ad-hoc connection delays of 25.9 seconds and reliable connection success rates within 6 meters underpin the approach's technical feasibility.

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Context Data Categories and Privacy Model for Mobile Data Collection Apps

Cited by 48 publications

References 31 publications

Longitudinal Experience–Wide Association Studies—A Framework for Studying Personality Change

Longitudinal Experience–Wide Association Studies—A Framework for Studying Personality Change

Sensing sociability: Individual differences in young adults’ conversation, calling, texting, and app use behaviors in daily life.

On gossip-based information dissemination in pervasive recommender systems

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