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"It's like a pet...but my pet doesn't collect data about me": Multi-person Households' Privacy Design Preferences for Household Robots

Jennica Li, Shirley Zhang, Dakota Sullivan, Bengisu Cagiltay, Heather Kirkorian, Bilge Mutlu, Kassem Fawaz

TL;DR

It is found that participants did not trust that robots, or their respective manufacturers, would respect the data privacy of household members or operate in a multi-user ecosystem without jeopardizing users'personal data.

Abstract

Household robots boasting mobility, more sophisticated sensors, and powerful processing models have become increasingly prevalent in the commercial market. However, these features may expose users to unwanted privacy risks, including unsolicited data collection and unauthorized data sharing. While security and privacy researchers thus far have explored people's privacy concerns around household robots, literature investigating people's preferred privacy designs and mitigation strategies is still limited. Additionally, the existing literature has not yet accounted for multi-user perspectives on privacy design and household robots. We aimed to fill this gap by conducting in-person participatory design sessions with 15 households to explore how they would design a privacy-aware household robot based on their concerns and expectations. We found that participants did not trust that robots, or their respective manufacturers, would respect the data privacy of household members or operate in a multi-user ecosystem without jeopardizing users' personal data. Based on these concerns, they generated designs that gave them authority over their data, contained accessible controls and notification systems, and could be customized and tailored to suit the needs and preferences of each user over time. We synthesize our findings into actionable design recommendations for robot manufacturers and developers.

"It's like a pet...but my pet doesn't collect data about me": Multi-person Households' Privacy Design Preferences for Household Robots

TL;DR

It is found that participants did not trust that robots, or their respective manufacturers, would respect the data privacy of household members or operate in a multi-user ecosystem without jeopardizing users'personal data.

Abstract

Household robots boasting mobility, more sophisticated sensors, and powerful processing models have become increasingly prevalent in the commercial market. However, these features may expose users to unwanted privacy risks, including unsolicited data collection and unauthorized data sharing. While security and privacy researchers thus far have explored people's privacy concerns around household robots, literature investigating people's preferred privacy designs and mitigation strategies is still limited. Additionally, the existing literature has not yet accounted for multi-user perspectives on privacy design and household robots. We aimed to fill this gap by conducting in-person participatory design sessions with 15 households to explore how they would design a privacy-aware household robot based on their concerns and expectations. We found that participants did not trust that robots, or their respective manufacturers, would respect the data privacy of household members or operate in a multi-user ecosystem without jeopardizing users' personal data. Based on these concerns, they generated designs that gave them authority over their data, contained accessible controls and notification systems, and could be customized and tailored to suit the needs and preferences of each user over time. We synthesize our findings into actionable design recommendations for robot manufacturers and developers.
Paper Structure (31 sections, 2 figures, 2 tables)

This paper contains 31 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Privacy Perception of Smart Home Device
  4. Security and Privacy of Household Robots
  5. The Current Study: Improving the Privacy and Security of Household Robots
  6. Methods
  7. Recruitment and Participants
  8. Materials and Procedure
  9. Co-Design Materials
  10. Study Procedure
  11. Robot Selection
  12. Data Analysis
  13. Privacy Designs for Household Robots
  14. Reasons for Privacy Designs
  15. Types of Privacy Design
  16. ...and 16 more sections

Figures (2)

  • Figure 1: Overview of Study Procedure. Participants first completed a pre-study interview covering their household management and their technology perception. They are then invited to an in-person interview with a co-design session.
  • Figure 2: A set of privacy-aware features on a robot, including different modes, a mapping system, a data collection and management system, a user profile and authentication system, and a notification system.