In this position paper, we discuss the merits of simulating privacy dynamics in recommender systems. We study this issue at hand from two perspectives: Firstly, we present a conceptual approach to integrate privacy into recommender system simulations, whose key elements are privacy agents. These agents can enhance users' profiles with different privacy preferences, e.g., their inclination to disclose data to the recommender system. Plus, they can protect users' privacy by guarding all actions that could be a threat to privacy. For example, agents can prohibit a user's privacy-threatening actions or apply privacy-enhancing techniques, e.g., Differential Privacy, to make actions less threatening. Secondly, we identify three critical topics for future research in privacy-aware recommender system simulations: (i) How could we model users' privacy preferences and protect users from performing any privacy-threatening actions? (ii) To what extent do privacy agents modify the users' document preferences? (iii) How do privacy preferences and privacy protections impact recommendations and privacy of others? Our conceptual privacy-aware simulation approach makes it possible to investigate the impact of privacy preferences and privacy protection on the micro-level, i.e., a single user, but also on the macro-level, i.e., all recommender system users. With this work, we hope to present perspectives on how privacy-aware simulations could be realized, such that they enable researchers to study the dynamics of privacy within a recommender system.
翻译:在这份立场文件中,我们讨论了在建议者系统中模拟隐私动态的好处。我们从两个角度研究这一问题:首先,我们提出将隐私纳入建议者系统模拟的概念方法,其关键要素是隐私代理。这些代理者可以以不同的隐私偏好加强用户概况,例如他们向建议者系统披露数据的倾向。此外,他们可以通过保护可能对隐私构成威胁的所有行动来保护用户隐私。例如,代理者可以禁止用户的隐私威胁行动,或者采用增强隐私的技术,例如差异隐私,以减少行动的威胁。第二,我们为未来对隐私意识建议者系统模拟的研究确定了三个关键议题:(一) 我们如何以不同隐私偏好模式来模拟用户的隐私,保护用户不从事任何威胁隐私的行动? (二) 隐私代理者在多大程度上会改变用户的文件偏好? (三) 隐私偏好和隐私保护如何影响他人的建议和隐私?我们的概念隐私认知模拟方法使得有可能调查隐私偏好隐私和保护对微观系统的影响,也建议了当前系统内部的用户视角。