Privacy policies contain important information regarding the collection and use of user’s data. As Internet of Things (IoT) devices have become popular during the last years, these policies have become important to protect IoT users from unwanted use of private data collected through them. However, IoT policies tend to be long thus discouraging users to read them. In this paper, we seek to create an automated and annotated corpus for IoT privacy policies through the use of natural language processing techniques. Our method extracts the purpose from privacy policies and allows users to quickly find the important information relevant to their data collection/use.
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Automatic Classification of Web and IoT Privacy Policies
Privacy policies, despite the important information they provide about the collection and use of one's data, tend to be skipped over by most Internet users. In this paper, we seek to make privacy policies more accessible by automatically classifying web privacy. We use natural language processing techniques and multiple machine learning models to determine the effectiveness of each method in the classification method. We also explore the effectiveness of these methods to classify privacy policies of Internet of Things (IoT) devices.
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- Award ID(s):
- 1950416
- NSF-PAR ID:
- 10376459
- Date Published:
- Journal Name:
- REUNS 2022: The 8th National Workshop for REU Research in Networking and Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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