A sociotechnical threat model for AI-driven smart home devices
这条记录已有公开讨论或多来源信号,适合验证热度、争议点和后续影响。
一项研究通过对18位英国住家家政服务人员的半结构化访谈,并结合传播隐私管理(CPM)理论进行威胁建模分析,探讨了AI智能家居设备给家政服务人员带来的隐私风险。研究发现,AI分析、数据日志和跨家庭数据流加剧了这些风险。在家政服务人员受雇的家庭中,AI功能和不透明的雇佣关系强化了监控,限制了他们协商隐私边界的能力。…
View PDF HTML (experimental)
Abstract: 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.
Comments: Paper accepted for presentation at Symposium on Usable Security and Privacy (USEC) 2026
Subjects:
Computers and Society (cs.CY) ; Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2602.09239 [cs.CY]
(or arXiv:2602.09239v1 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2602.09239
arXiv-issued DOI via DataCite
Submission history
From: Shijing He [ view email ] [v1] Mon, 9 Feb 2026 22:08:02 UTC (58 KB)