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

Abstract

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.

Usability and experience testing to refine an online intervention to prevent weight gain in new kidney transplant recipients

“…Threats to user privacy is a challenge in digital health care (Blandford, 2019). Studies investigating user perceptions with digital health interventions suggest mental health data is perceived to be the most sensitive (Stawarz, Preist, Tallon, Wiles, & Coyle, 2018), in comparison to general and physical health data (Valdez & Ziefle, 2019). In this current study, there were no concerns raised about data security and privacy from participants.…”

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

“…Here, the most important factor for users' decision to share data was the data type. For sharing medical data, two anonymization techniques, k-anonymity and differential privacy, have been compared in their impact on sharing decisions by Calero Valdez and Ziefle (2019). Regardless of the anonymization method, anonymization was the most important factor for the participants in this study and the type of benefit for data sharing was less important.…”

“…The willingness to provide data varies between data types and the type of data is the second most important attribute of the conjoint study. The sensitivity of information plays an important role for the risk perceptions and the willingness to share data (Calero Valdez and Ziefle 2019;Milne et al 2016;Wirth et al 2019). Users are willing to provide (certain) data when it has a benefit for themselves or the society and when trust in the data protection is prevalent.…”

Rolling in the deep

“…Not only minor technical knowledge levels but also over-trust in having control of data might be responsible for observed privacy behaviors (Schomakers et al 2019b;. Recent research showed that the majority of users are quite sensitive in the context of data exchange and privacy issues, especially when the data is used by third parties without public transparency (Lidynia et al 2017;Valdez and Ziefle 2019). Another critical issue for users concerns the question for how long data may be stored and which authority is responsible for the storage.…”

“…The longer the data storage and the more data is stored on servers beyond the control of the users (e.g., central servers of companies or the traffic management), the lower is the willingness to share data, independently of the type of data (Schmidt et al 2015a). Concerns are also higher the more personal the information is and the higher the probability of being identifiable (Valdez and Ziefle 2019;Ziefle et al 2016). However, there is also empirical evidence that people seem to be differently vulnerable for those concerns (Schmidt et al 2015a;Schomakers et al 2018;Schomakers et al 2019b).…”