Design heuristics: privacy and portability Regulation as a feature request
September 12, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yasodara Cordova
arXiv ID
2209.05384
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CR
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
The lack of user experience standards in regulations for data privacy and data portability in the health sector increases the cost of leaving a network provider while not protecting the patient's privacy, directly impacting people's health. Furthermore, user in-app options for data sharing and portability in the health sector's applications make it difficult to transfer data between providers while facilitating privacy breaches. Moreover, it leaves users unaware of occasional past unauthorized data access episodes. In this article, we propose an extension for the traditional design heuristics to increase privacy and portability controls for applications that deal with users' personal information based on a benchmark in applications from different sectors and a literature review.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted