The growing adoption of AI-driven smart home devices has introduced new privacy risks for domestic workers (DWs), who are frequently monitored in employers' homes while also using smart devices in their own households. We conducted semi-structured interviews with 18 UK-based DWs and performed a human-centered threat modeling analysis of their experiences through the lens of Communication Privacy Management (CPM). Our findings extend existing threat models beyond abstract adversaries and single-household contexts by showing how AI analytics, residual data logs, and cross-household data flows shaped the privacy risks faced by participants. In employer-controlled homes, AI-enabled features and opaque, agency-mediated employment arrangements intensified surveillance and constrained participants' ability to negotiate privacy boundaries. In their own homes, participants had greater control as device owners but still faced challenges, including gendered administrative roles, opaque AI functionalities, and uncertainty around data retention. We synthesize these insights into a sociotechnical threat model that identifies DW agencies as institutional adversaries and maps AI-driven privacy risks across interconnected households, and we outline social and practical implications for strengthening DW privacy and agency.

翻译：随着AI驱动智能家居设备的日益普及，住家工作者正面临新的隐私风险——他们在雇主家中常处于监控之下，同时也在自己家中使用智能设备。我们通过对18位英国住家工作者的半结构化访谈，并运用沟通隐私管理理论框架对其经历进行以人为中心的威胁建模分析。研究发现：AI分析系统、残留数据日志和跨家庭数据流共同塑造了参与者面临的隐私风险，这使现有威胁模型得以突破抽象对抗者和单家庭情境的局限。在雇主控制的家庭中，AI赋能功能与不透明的中介雇佣安排加剧了监控强度，限制了参与者协商隐私边界的能力。在自有住宅中，参与者作为设备所有者拥有更高控制权，但仍面临包括性别化的管理角色、不透明的AI功能以及数据留存不确定性在内的多重挑战。我们整合这些发现构建了一个社会技术威胁模型，该模型将住家服务中介机构识别为制度性对抗者，并描绘了AI驱动的隐私风险在互联家庭网络中的传播路径，最后提出了增强住家工作者隐私保护与自主能力的社会实践启示。