The users' perspective on the privacy-utility trade-offs in health recommender systems. Issue 121 (January 2019)

• Record Type: Journal Article
• Authors: Calero Valdez, André; Ziefle, Martina
• Is Part Of: International journal of human-computer studies. Issue 121(2019)
• Page Start: 108
• Page End: 121
• Publication Date: 2019-01
• Subjects: Recommender systems -- Health recommender system -- Privacy -- Privacy trade-off -- Health informatics -- Conjoint study; 00-01 -- 99-00; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2018.04.003
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: We investigated users perceptions of privacy-utility trade-offs in two studies. We investigated perception on k-anonymity and differential privacy. We used two conjoint decision tasks to estimate the worth of privacy. We found commercial use of data to be detrimental to acceptance. 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.