The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems

TL;DR

This study explores users' willingness to share health data in recommender systems, revealing preferences and concerns about privacy, data type, and purpose, with implications for designing privacy-aware health services.

Contribution

It provides empirical insights into user preferences and privacy concerns in health recommender systems using conjoint-decision studies.

Findings

Users oppose sharing health data for commercial purposes, especially mental health.

Users are more willing to share data for scientific research and physical health.

Concerns about de-anonymization risks influence sharing willingness.

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.