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- W2567992210 abstract "Despite the best efforts of usable privacy community to ease the burden of understanding privacy policies, there is still a lack in having a notice display that helps users to compare different application alternatives based on their data gathering practices. In this work, we propose an approach to quantify the amount of data collection of an application by analyzing its privacy policy text using natural language processing (NLP) techniques. There are in fact numerous use cases for such a quantitative measure, one of which is designing a visceral notice that relies on an experiential approach to communicate privacy information to users. The results show that our quantification approach holds promise. Using our quantification measure, we propose a new display for nano-sized visceral notice in which we leverage user's familiarity with pie chart as a data measuring tool to communicate information about an app's data collection practice." @default.
- W2567992210 created "2017-01-13" @default.
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- W2567992210 date "2016-11-01" @default.
- W2567992210 modified "2023-09-27" @default.
- W2567992210 title "If You Can't Measure It, You Can't Manage It: Towards Quantification of Privacy Policies" @default.
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- W2567992210 doi "https://doi.org/10.1109/cic.2016.082" @default.
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