Matcha: An IDE Plugin for Creating Accurate Privacy Nutrition Labels
CoRR(2024)
摘要
Apple and Google introduced their versions of privacy nutrition labels to the
mobile app stores to better inform users of the apps' data practices. However,
these labels are self-reported by developers and have been found to contain
many inaccuracies due to misunderstandings of the label taxonomy. In this work,
we present Matcha, an IDE plugin that uses automated code analysis to help
developers create accurate Google Play data safety labels. Developers can
benefit from Matcha's ability to detect user data accesses and transmissions
while staying in control of the generated label by adding custom Java
annotations and modifying an auto-generated XML specification. Our evaluation
with 12 developers showed that Matcha helped our participants improved the
accuracy of a label they created with Google's official tool for a real-world
app they developed. We found that participants preferred Matcha for its
accuracy benefits. Drawing on Matcha, we discuss general design recommendations
for developer tools used to create accurate standardized privacy notices.
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