Summary of Identifying Privacy Personas, by Olena Hrynenko and Andrea Cavallaro
Identifying Privacy Personas
by Olena Hrynenko, Andrea Cavallaro
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes eight novel privacy personas by combining qualitative and quantitative analysis of responses to an interactive educational questionnaire. These personas capture differences in user segments based on knowledge, behavioral patterns, self-efficacy, and perceived importance of privacy protection. The proposed approach uses divisive hierarchical clustering and Boschloo’s statistical test of homogeneity of proportions to ensure the elicited clusters differ statistically. Additionally, a new measure for calculating distances between questionnaire responses is introduced, accounting for question types (closed- vs open-ended). The paper shows that the proposed personas statistically differ from each other and provides a more granular and comprehensive understanding of user segments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates eight unique privacy personas to help understand how different people think about privacy. These personas are based on people’s answers to questions in an interactive questionnaire. The researchers used special statistical methods to make sure the groups they found were truly different from each other. They also developed a new way to measure how similar or different people’s answers are, depending on whether the question is closed-ended (like multiple choice) or open-ended (like writing a paragraph). The study shows that these personas can help better understand and assist people with their privacy needs. |
Keywords
» Artificial intelligence » Hierarchical clustering
