用户视角下健康推荐系统中的隐私-效用权衡
The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems
AI总结：
该研究通过两项 n=521 的联合决策实验，考察用户在健康推荐系统中对 k-匿名与差分隐私等保护机制及数据分享场景的偏好，并据此提出开发建议。
摘要：
对于推荐系统等个性化服务的用户而言，隐私是一项重要利益。当其应用于健康信息学领域时，用户的隐私担忧可能会被放大，但此类服务可能带来的效用也很高。尽管已有 k-匿名、差分隐私、隐私感知推荐和个性化隐私权衡等技术可用，但关于用户是否愿意分享健康数据以供此类系统使用的研究仍然很少。在两项联合决策研究中（样本量 n=521），我们调查了与个人健康数据分享相关的隐私保护技术在 k-匿名和差分隐私方面的重要性与效用。研究要求用户根据数据接收方、分享数据的收益、数据类型以及参数化隐私来选择偏好的分享场景。用户不赞成为商业目的分享与精神疾病相关且具有较高去匿名化风险的数据，但当数据用于科学目的且与身体疾病相关时，用户几乎不表示担忧。研究结果为健康推荐系统的开发提出了建议。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
