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Justin Reed Basdeo

dblp:239/7921 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 87% Design research and methods · 13%
Network and information security
1 paper
Privacy and data protection · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Ubiquitous computing and smart environments
smart home
0.412019
Defending My Castle: A Co-Design Study of Privacy Mechanisms for Smart Homes · CHI 2019
Ubiquitous computing and smart environments › smart home
smart home privacy
0.412019
Defending My Castle: A Co-Design Study of Privacy Mechanisms for Smart Homes · CHI 2019
Design research and methods
participatory design
0.112019
Defending My Castle: A Co-Design Study of Privacy Mechanisms for Smart Homes · CHI 2019

Methods — techniques the papers use, named apart from their topics

co-design study · 0.8
YearPublicationVenuePosition
2019 Defending My Castle: A Co-Design Study of Privacy Mechanisms for Smart Homes
abstract
Home is a person's castle, a private and protected space. Internet-connected devices such as locks, cameras, and speakers might make a home "smarter" but also raise privacy issues because these devices may constantly and inconspicuously collect, infer or even share information about people in the home. To explore user-centered privacy designs for smart homes, we conducted a co-design study in which we worked closely with diverse groups of participants in creating new designs. This study helps fill the gap in the literature between studying users' privacy concerns and designing privacy tools only by experts. Our participants' privacy designs often relied on simple strategies, such as data localization, disconnection from the Internet, and a private mode. From these designs, we identified six key design factors: data transparency and control, security, safety, usability and user experience, system intelligence, and system modality. We discuss how these factors can guide design for smart home privacy.
Yaxing Yao, Justin Reed Basdeo, Smirity Kaushik, Yang Wang 0005
CHI2
2019 Privacy Perceptions and Designs of Bystanders in Smart Homes
abstract
As the Internet of Things (IoT) devices make their ways into people's homes, traditional dwellings are turning into smart homes. While prior empirical studies have examined people's privacy concerns of smart homes and their desired ways of mitigating these concerns, the focus was primarily on the end users or device owners. Our research investigated the privacy perceptions and design ideas of smart home bystanders, i.e., people who are not the owners nor the primary users of smart home devices but can potentially be involved in the device usage, such as other family members or guests. We conducted focus groups and co-design activities with eighteen participants. We identified three impacting factors of bystanders' privacy perceptions (e.g., perceived norms) and a number of design factors to mitigate their privacy concerns (e.g., asking for device control). We highlighted bystanders' needs for privacy and controls, as well as the tension of privacy expectations between the owners/users and the bystanders in smart homes. We discussed how future designs can better support and balance the privacy needs of different stakeholders in smart homes.
Yaxing Yao, Justin Reed Basdeo, Oriana Rosata Mcdonough, Yang Wang 0005
Proc. ACM Hum. Comput. Interact.2