Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants' perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people's privacy preferences. The features of the collected modality define data modality features -- spatial, security, and temporal context. In contrast, personal features consist of one's awareness of data modality features and data inferences, definitions of privacy and security, and the available rewards and utility. Our proposed model of people's privacy preferences in smart office buildings helps design more effective measures to improve people's privacy.
翻译:建筑管理系统能带来许多好处,例如能源效率和舒适,但又依赖来自各种传感器的大量数据。机器学习算法的进步使得有可能获取关于占用者及其活动的个人信息,而超出非侵入性传感器的预期设计范围。然而,占用者并不了解数据收集,拥有不同的隐私偏好和隐私损失门槛。虽然智能之家最了解隐私观点和偏好,但有限的研究对智能办公大楼中的这些因素进行了评估,因为那里有更多的用户和不同的隐私风险。为了更好地了解占用者的看法和隐私偏好,我们在2022年4月至2022年5月期间对智能办公大楼的占用者进行了24次半结构性访谈。我们发现,数据模式的特征和个人特征有助于人们的隐私偏好。所收集的模式特征界定了数据模式的特征 -- -- 空间、安全和时间背景。相比之下,个人特征包括对数据模式特征和数据推断的认识、隐私和安全的定义以及现有的奖赏和效用。我们提议的智能办公大楼中人们的隐私偏好模式有助于设计更有效的改善隐私的措施。