New Differential Privacy Communication Pipeline and Design Framework
August 04, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jingyu Jia, Zikai Alex Wen, Zheli Liu, Changyu Dong
arXiv ID
2208.02759
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CR
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Organizations started to adopt differential privacy (DP) techniques hoping to persuade more users to share personal data with them. However, many users do not understand DP techniques, thus may not be willing to share. Previous research suggested that the design of DP mechanism communication could influence users' willingness to share data. Based on the prior work, we propose a new communication pipeline that starts by asking users about their privacy concerns and then provides a customized DP mechanism and communication. We also propose a design framework that systemically explores effective communication designs ranging from a text-based high-level description to a step-by-step interactive storyboard. Based on the framework, we created 17 designs and recruited five people to evaluate. Our user study showed that text-based descriptions have the highest clarity in all scenarios, while the step-by-step interactive storyboards have the potential to persuade users to trust central DP. Our future work will optimize the design and conduct a large-scale efficacy study.
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