You 'Might' Be Affected: An Empirical Analysis of Readability and Usability Issues in Data Breach Notifications
Authors
Data breaches place affected individuals at significant risk of identity theft. Yet, prior studies have shown that many consumers do not take protective actions after receiving a data breach notification from a company. We analyzed 161 data breach notifications sent to consumers with respect to their readability, structure, risk communication, and presentation of potential actions. We find that notifications are long and require advanced reading skills. Many companies downplay or obscure the likelihood of the receiver being affected by the breach and associated risks. Moreover, potential actions and offered compensations are frequently described in lengthy paragraphs instead of clearly listed. Little information is provided regarding an action's urgency and effectiveness; little guidance is provided on which actions to prioritize. Based on our findings, we provide recommendations for designing more usable and informative data breach notifications that could help consumers better mitigate the consequences of being affected by a data breach.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 100%
SIGCHI Social Impact Award Talk – Making Privacy and Security More Usable
CHI '18· Privacy by Design & User Control +1
- 100%
Human-GDPR Interaction: Practical Experiences of Accessing Personal Data
CHI '22· Privacy by Design & User Control +1
- 100%
Obfuscation Remedies Harms Arising from Content Flagging of Photos
CHI '22· Privacy by Design & User Control +1
- 100%
Understanding Privacy Switching Behaviour on Twitter
CHI '22· Privacy by Design & User Control +1
- 100%
How Language Formality in Security and Privacy Interfaces Impacts Intended Compliance
CHI '23· Privacy by Design & User Control +1
- 100%
The Impact of Risk Appeal Approaches on Users’ Sharing Confidential Information
CHI '24· Privacy by Design & User Control +1
- 75%
Contextualizing Privacy Decisions for Better Prediction (and Protection)
CHI '18· Privacy by Design & User Control +1
- 75%
“This App Would Like to Use Your Current Location to Better Serve You”: Importance of User Assent and System Transparency in Personalized Mobile Services
CHI '18· Privacy by Design & User Control +1
- 75%
A Field Study of Computer-Security Perceptions Using Anti-Virus Customer-Support Chats
CHI '19· Privacy by Design & User Control +1
- 75%
Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information
CHI '19· Privacy by Design & User Control +1
Based on Jaccard similarity of research subtopics & professions (≥60%)