"Privacy across the boundary": Examining Perceived Privacy Risk Across Data Transmission and Sharing Ranges of Smart Home Personal Assistants

TL;DR

This study investigates how users perceive privacy risks in smart home personal assistants, revealing that crossing certain boundaries significantly increases perceived risks and highlighting limitations of anonymization safeguards.

Contribution

It empirically applies Privacy Boundary Theory to SPAs, identifying key boundary-related risk factors and evaluating the effectiveness of anonymization in privacy protection.

Findings

Perceived risk escalates when data crosses public network and third-party boundaries.

Risk perception varies with data attributes and contextual factors.

Anonymization is limited in effectiveness, especially for third-party data sharing.

Abstract

As Smart Home Personal Assistants (SPAs) evolve into social agents, understanding user privacy necessitates interpersonal communication frameworks, such as Privacy Boundary Theory (PBT). To ground our investigation, our three-phase preliminary study (1) identified transmission and sharing ranges as key boundary-related risk factors, (2) categorized relevant SPA functions and data types, and (3) analyzed commercial practices, revealing widespread data sharing and non-transparent safeguards. A subsequent mixed-methods study (N=412 survey, N=40 interviews among the survey participants) assessed users' perceived privacy risks across data types, transmission ranges and sharing ranges. Results demonstrate a significant, non-linear escalation in perceived risk when data crosses two critical boundaries: the `public network' (transmission) and `third parties' (sharing). This boundary effect holds robustly across data types and demographics. Furthermore, risk perception is modulated by data attributes (e.g., social relational data), and contextual privacy calculus. Conversely, anonymization safeguards show limited efficacy especially for third-party sharing, a finding attributed to user distrust. These findings empirically ground PBT in the SPA context and inform design of boundary-aware privacy protection.