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About Uipicker: User-Input Privacy Identification in Mobile Applications
This document summarizes a research paper that presents UIPicker, a framework for automatically identifying sensitive user input (UIP) data in mobile apps. The paper finds that over 35% of popular apps require sensitive user inputs like credit card numbers or passwords, but existing techniques can only label data accessed through APIs, not data entered by users. UIPicker uses natural language processing and machine learning to analyze app layout resources and code to detect where private user inputs may occur. It was evaluated on 200 apps and achieved 93.6% precision and 90.1% recall in identifying sensitive user inputs. The paper contributes an approach for automatic, large-scale identification of sensitive user data entered by users in apps.
Detailed Information
| Author: | ['Ravneet Arora'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 17 |
| Language: | English |
| Format: | |
| Price: | FREE |
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