Data Privacy and Security with Smart Home Devices
Duration: 7 weeks
Collaborators: Spoorthi Cheriverala, Shannon Lin, Chelsea Tang
Tools:, Figma, User Research, Language Processing, Miro, Prototyping

Smart home devices like Amazon’s Alexa and Google Home have made a name for themselves in households around the globe. The many functions that these devices provide help make daily life more convienient and efficient. However, their rise in popularity has also brought about a concerns surrounding these devices and their potential to collect data and breach privacy. Our group set out to understand why people harbored these concerns and improve transparency between users and technology through a human-centered design lense.  

Problem Space

To understand the problem space, our group conducted a series of interviews with college students and field experts; we also took data from the 50+ responses we got through our google form. We identified user wants & needs that helped us design user-centered solutions to help improve the transparency of information regarding collected user information and privacy policies between technologies and users. Our solutions were focused on improving existing interfaces and organization of information.


Viewers are given the context behind the usage of smart home devices through a list of statistics. We give our reasoning behind our choice to improve transparency and privacy for smart home device applications.

A balance exists between the potential in personalization and an intrusion of privacy [...] Exploring what options are available to share, and how companies utilize said options shared to them, grants greater understanding in the benefits or ramifications of customization.

Research Methods

Quantitative Methods:
  • Google Form Survey: Sent out a initial form for participant feedback
  • Identified whether participants use smart home devices based on their demographics
  • Asked whether or not they would be able to participate in a recorded interview
  • Asked surface level questions on data security/privacy

Qualitative Methods
  • Audio recorded interviews: Transcribed
  • Used language data visualizer to detect keywords and commonly reoccurring terms
  • Located in a private and quiet space for clear video recording
  • Recorded responses to interview questions to identity common trends and synthesize insights

Stakeholder Map

To left is a list of individuals or groups that are affected or involved with smart home devices.

Data Analysis

Using language processing, I was able to identify reoccuring key words and themes in our interviews. We found that smart home device user’s used “know (173)” while non-users using “think (103)” answering questions relating to smart home devices. We gathered that non-users tended to assume when answering questions about device usage.
The Google form revealed that the younger generation (17-24) tended to be less skeptical about smart home devices while the older generation (40-70) tended to be more skeptical about smart home devices.

User Centered Solution 1 focused on improving awareness about existing smart home device features in regards to data security and privacy.

User Centered Solution 2 focused on redesigning UI to improve app flow, clarity, organization, and information flow.
Assumptions & Weaknesses

  • Larger sample size required for more accurate data
  • Conducting interviews beyond Carnegie Mellon University students
  • Reorganizing how we asked questions in our Google Form

  • Assumed that people who were originally skeptical about smart home devices would trust information about smart home devices
  • Young people are generally more receptive to technology
  • People use the Alexa/smart home app

Anthony Pan © 2022