Users’ Willingness to Share Data on the Internet: Perceived Benefits and Caveats

Ziefle, Martina; Halbey, Julian; Kowalewski, Sylvia

doi:10.5220/0005897402550265

Cited by 24 publications

(22 citation statements)

References 36 publications

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“…Levels were chosen from the focus group results to find levels that are sufficiently different from a users' perspective. This approach has been successfully applied to privacy perceptions in other contexts (Ziefle et al, 2016). The findings from such studies help understand how users evaluate criteria in a conjoint-setting.…”

Section: Understanding Decisions To Share Data: the Conjoint Analysismentioning

confidence: 99%

The users’ perspective on the privacy-utility trade-offs in health recommender systems

Valdez

Ziefle

2019

International Journal of Human-Computer Studies

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Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n = 521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.

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Section: Understanding Decisions To Share Data: the Conjoint Analysismentioning

confidence: 99%

The users’ perspective on the privacy-utility trade-offs in health recommender systems

Valdez

Ziefle

2019

International Journal of Human-Computer Studies

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“…The willingness to share data is dependent on many factors, e.g., privacy concerns, benefits, type of information, and culture (Hallam and Zanella 2017;Markos et al 2017;Trepte et al 2017). This willingness to share has been extensively studied (Smith et al 2011) and some studies were conducted within the context of data markets and included privacy protection in data sharing (Roeber et al 2015;Ziefle et al 2016). But we do not yet have the necessary insights into what informational self-determination and privacy means to users in such data markets.…”

Section: Introductionmentioning

confidence: 99%

All of me? Users’ preferences for privacy-preserving data markets and the importance of anonymity

2020

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Privacy-preserving data markets are one approach to restore users' online privacy and informational self-determination and to build reliable data markets for companies and research. We empirically analyze internet users' preferences for privacy in data sharing, combining qualitative and quantitative empirical methods. Study I aimed at uncovering users' mental models of privacy and preferences for data sharing. Study II quantified and confirmed motives, barriers, and conditions for privacy in data markets. Finally, in a conjoint study, trade-offs between decisive attributes that shape the decision to share data are analyzed. Additionally, differences between user groups with high and with low privacy concerns are observed. The results show that the anonymization level has the greatest impact on the willingness to share data, followed by the type of data. Users with higher privacy concerns are less willing to share data in data markets and want more privacy protection. The results contribute to an understanding of how privacy-preserving data markets could be designed to suit users' preferences.

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“…On the other hand many users openly agree with sharing private information, even when aware of risks. Finding the matching trade-off between privacy and utility for the individual user group or user is crucial to implementing health recommender systems [57]. In this context, individual factors play a decisive role.…”

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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