Like us on Facebook! – Analyzing User Preferences Regarding Privacy Settings in Germany

Kowalewski, Sylvia; Ziefle, Martina; Ziegeldorf, Henrik; Wehrle, Klaus

doi:10.1016/j.promfg.2015.07.336

Cited by 15 publications

(9 citation statements)

References 17 publications

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“…Control over personal information constitutes a whole dimension of privacy concerns for users ( [24], [31]). Interestingly, perceived control over information does not seem to impact the level of related privacy concerns, whenever this control is perceived to be low [26]. That result is in line with findings on feeling resigned regarding one's privacy ( [50], discussed in Section 2), and that a perceived higher level of control may increase the willingness to disclose personal information [6].…”

Section: Control Feedback and Privacy Researchsupporting

confidence: 78%

Is Privacy Controllable?

Shulman

Meyer

2019

Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data

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One of the major views of privacy associates privacy with the control over information. This gives rise to the question how controllable privacy actually is. In this paper, we adapt certain formal methods of control theory and investigate the implications of a control theoretic analysis of privacy. We look at how control and feedback mechanisms have been studied in the privacy literature. Relying on the control theoretic framework, we develop a simplistic conceptual control model of privacy, formulate privacy controllability issues and suggest directions for possible research.

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Section: Control Feedback and Privacy Researchsupporting

confidence: 78%

Is Privacy Controllable?

Shulman

Meyer

2019

Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data

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“…We found a negative relation between age and willingness; as users grow older, they tend to be more reluctant about data sharing, with pensioners having the highest percentage of negative responses (376/960, 39.17%). Older age has been connected with a lack of knowledge over factual risks and growing concerns over possible data misuse and security breaches [53,70]. Aging has been associated with the development of health conditions.…”

Section: Discussionmentioning

confidence: 99%

Connected Health User Willingness to Share Personal Health Data: Questionnaire Study

Karampela¹,

Ouhbi²,

Isomursu³

2019

J Med Internet Res

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BackgroundConnected health has created opportunities for leveraging health data to deliver preventive and personalized health care services. The increasing number of personal devices and advances in measurement technologies contribute to an exponential growth in digital health data. The practices for sharing data across the health ecosystem are evolving as there are more opportunities for using such data to deliver responsive health services.ObjectiveThe objective of this study was to explore user attitudes toward sharing personal health data (PHD). The study was executed within the first year after the implementation of the new General Data Protection Regulation (GDPR) legal framework.MethodsThe authors analyzed the results of an online questionnaire survey to explore the willingness of 8004 people using connected health services across four European countries to share their PHD and the conditions under which they would be willing to do so.ResultsOur findings indicate that the majority of users are willing to share their personal PHD for scientific research (1811/8004, 22.63%). Age, education level, and occupation of the participants, in addition to the level of digitalization in their country were found to be associated with data sharing attitudes.ConclusionsPositive attitudes toward data sharing for scientific research can be perceived as an indication of trust established between users and academia. Nevertheless, the interpretation of data sharing attitudes is a complex process, related to and influenced by various factors.

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“…In this context, individual factors play a decisive role. As such, the level of knowledge about privacy threats in the Internet is important [58], but also different risk perceptions, as well as the level of digital competency [59], which is often related to age and technology generation [60,61]. Factual (technical) privacy threat is furthermore not identical with risk perceptions.…”

Section: Methods To Design Health Recommender Systemsmentioning

confidence: 99%

“…Factual (technical) privacy threat is furthermore not identical with risk perceptions. The perceived benefits from sharing the data in the medical context is seen different than sharing the data in a less sensitive field [62,58,63]. In addition, users are much more reluctant to share data in personal spaces, when data relate to intimacy contexts, such as homes [64] or the sharing of physiological data [65].…”

Section: Methods To Design Health Recommender Systemsmentioning

confidence: 99%

Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives

Valdez

Ziefle

Verbert

et al. 2016

Lecture Notes in Computer Science

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Recommender systems are a classical example for machine learning applications, however, they have not yet been used extensively in health informatics and medical scenarios. We argue that this is due to the specifics of benchmarking criteria in medical scenarios and the multitude of drastically differing end-user groups and the enormous contextcomplexity of the medical domain. Here both risk perceptions towards data security and privacy as well as trust in safe technical systems play a central and specific role, particularly in the clinical context. These aspects dominate acceptance of such systems. By using a Doctor-in-the-Loop approach some of these difficulties could be mitigated by combining both human expertise with computer efficiency. We provide a three-part research framework to access health recommender systems, suggesting to incorporate domain understanding, evaluation and specific methodology into the development process.

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Like us on Facebook! – Analyzing User Preferences Regarding Privacy Settings in Germany

Cited by 15 publications

References 17 publications

Is Privacy Controllable?

Is Privacy Controllable?

Connected Health User Willingness to Share Personal Health Data: Questionnaire Study

Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives

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